# pARam: Leveraging Parametric Design in Extended Reality to Support the
Personalization of Artifacts for Personal Fabrication

Evgeny Stemasov  0000-0002-3748-6441<EMAIL_ADDRESS>Ulm
UniversityUlmGermany ,  Simon Demharter  0000-0002-8768-2942
<EMAIL_ADDRESS>Ulm UniversityUlmGermany ,  Max Rädler
0000-0002-5413-2637<EMAIL_ADDRESS>Ulm UniversityUlmGermany ,  Jan
Gugenheimer  0000-0002-6466-3845<EMAIL_ADDRESS>TU-
DarmstadtDarmstadtGermany Institut Polytechnique de ParisParisFrance and
Enrico Rukzio  0000-0002-4213-2226<EMAIL_ADDRESS>Ulm
UniversityUlmGermany

(2024)

###### Abstract.

Extended Reality (XR) allows in-situ previewing of designs to be manufactured
through Personal Fabrication (PF). These in-situ interactions exhibit
advantages for PF, like incorporating the environment into the design process.
However, design-for-fabrication in XR often happens through either highly
complex 3D-modeling or is reduced to rudimentary adaptations of crowd-sourced
models. We present pARam, a tool combining parametric designs (PDs) and XR,
enabling in-situ configuration of artifacts for PF. In contrast to modeling-
or search-focused approaches, pARam supports customization through embodied
and practical inputs (e.g., gestures, recommendations) and evaluation (e.g.,
lighting estimation) without demanding complex 3D-modeling skills. We
implemented pARam for HoloLens 2 and evaluated it ($n=20$), comparing XR and
desktop conditions. Users succeeded in choosing context-related parameters and
took their environment into account for their configuration using pARam. We
reflect on the prospects and challenges of PDs in XR to streamline complex
design methods for PF while retaining suitable expressivity.

Personal Fabrication, Mixed Reality, Design Customization, 3D-modeling,
Parametric Designs, In-Situ Modeling, In-Situ Design, Remixing, Customizer
Interfaces, pARam

††journalyear: 2024††copyright: rightsretained††conference: Proceedings of the
CHI Conference on Human Factors in Computing Systems; May 11–16, 2024;
Honolulu, HI, USA††booktitle: Proceedings of the CHI Conference on Human
Factors in Computing Systems (CHI ’24), May 11–16, 2024, Honolulu, HI,
USA††doi: 10.1145/3613904.3642083††isbn: 979-8-4007-0330-0/24/05††ccs: Human-
centered computing Human computer interaction (HCI)††ccs: Human-centered
computing Mixed / augmented reality††ccs: Human-centered computing Interaction
design

Figure 1. pARam enables in-situ interaction with parametric designs through
extended reality. It allows users to position and preview fabricatable
parametric designs in the context they will be used in after fabrication (1).
Users then configure the designs to their liking and to their contextual
requirements, leveraging support mechanisms like mid-air sketching or
recommendations (2), and validate them with respect to existing objects or
their environment (e.g., through lighting estimation) in-situ (3). This allows
users to achieve fitting results that are fabricated based on their in-situ
customization (4) without engaging in (3D-)modeling “from scratch” or being
restricted to finished, immutable designs.

Personal fabrication (PF) is within reach of a broad range of users:
increasing numbers of people have access to the means of industry-grade
manufacturing devices, such as 3D-printers or laser cutters (Gershenfeld,
2005; Mota, 2011). This access is facilitated through device ownership or
infrastructure like makerspaces (Soomro et al., 2023), print centers (Hudson
et al., 2016), or fablabs (Garnier and Capdevila, 2021; Gershenfeld et al.,
2017). The means for personal fabrication empower end-users to create unique
artifacts following their requirements and express themselves in a physical,
tangible medium. Two contrasting approaches to designing for fabrication
exist: a) modeling, where users define most, if not all, details of a design
(Stemasov et al., 2021); and b) retrieval, where users search for finished
models (Alcock et al., 2016; Stemasov et al., 2021) in repositories like
Printables or Thingiverse, which provide access to crowd-sourced designs
(Liang et al., 2022). However, both approaches have their shortcomings.
3D-modeling for personal fabrication fundamentally requires users to invest
time in learning and applying _domain knowledge_ from various disciplines,
like design, engineering, or material science (Stemasov et al., 2021; Hudson
et al., 2016). Retrieval, on the other hand, is constrained by the available
databases and suffers from hard-to-alter designs (Alcock et al., 2016). A
middle ground is found in the notion of remixing. In the context of digital
media (Navas, 2014), creative programming (Hill and Monroy-Hernández, 2013)
but also in personal fabrication (Oehlberg et al., 2015), remixing allows
creative practitioners to benefit from collaborative effort and avoid
“starting from scratch” (Stemasov et al., 2021). For personal fabrication,
remixing is facilitated through interfaces to parametric designs:
“customizers” (Liang et al., 2022). This is particularly visible across model
repositories like Thingiverse 111https://www.thingiverse.com/app:22, Accessed:
02.02.2024 (Oehlberg et al., 2015), where the presence of these easy-to-use
interfaces to create adapted designs (remixes) sparked an increase of
contributions (Flath et al., 2017). Parametric designs constrain the virtually
infinite output space of a “traditional” design tool (e.g., Blender), to a
limited set of valid designs for a specific object defined through
combinations of parameters chosen by a “primary author” (Carpo, 2017). They,
therefore, implicitly embed domain knowledge provided by the creator of the
parametric design and may prevent errors (Shugrina et al., 2015; Stemasov et
al., 2021). Comparable interactions–from a user perspective–are already
present and are becoming increasingly relevant in consumer-facing systems like
product configurators for furniture222e.g., https://tylko.com/furniture-c/ or
https://www.pickawood.com/en/configurator/tables, Accessed: 01.02.2024,
footwear333e.g., https://www.bullfeet.com/en/custom-sport-shoes/personalize,
Accessed: 02.02.2024, or other trinkets444e.g.,
hhttps://www.shapeways.com/creator, Accessed: 29.01.2024.

Customizers constrain available input and output spaces. They balance
complexity and expressivity by allowing users to start with a finished object
definition, and tailor specific, relevant aspects of it to their liking.
However, they remain complex to successfully interact with, due to a set of
issues: 1) they _operate ex-situ_ , requiring transfers between physical
context and design environment (Ashbrook et al., 2016; Mahapatra et al.,
2019); 2) they often provide _little to no support_ in choosing specific
values for parameters beyond constraints (Shugrina et al., 2015); and 3) in
the context of personal fabrication, the _evaluation_ of consequences is left
to users and is often done through iteration, prototyping, and patching
(Teibrich et al., 2015). Ex-situ design means that novice users have to engage
in (error-prone) measurement and transfer procedures, diminishing their
chances of success (Mahapatra et al., 2019). The absence of woven-in domain
knowledge means that users have to explore and understand the effects of
parameters on geometry, but also on subsequent process steps, like
manufacturing (e.g., fabricability (Shugrina et al., 2015)) or usage (e.g.,
ergonomics (Lee et al., 2018)). This is linked to the issue of evaluation,
where a chosen parameter set may have non-trivial effects that users may only
see after fabrication (e.g., stylistic consistency, lighting patterns),
requiring them to iterate should they fail.

Based on formative explorations of product customizers, parameter types, and
literature, we developed pARam, a design tool to interact with, evaluate, and
preview parametric designs for personal fabrication in-situ. pARam leverages
the capabilities of Extended Reality (XR) devices to facilitate these
interactions, which were previously bound to desktop devices (_ex-situ_). It
was implemented as an application for the Microsoft HoloLens 2 and supports a
total of 15 parametric designs. pARam allows users to preview (Fig. 1.1)
customize a parametric design in-situ through a 2D interface, direct
manipulation, or gestures (Fig. 1.2). Lastly, before fabrication, users may
assess their design _in-situ_ : using the environment mesh acquired by the
HoloLens, they may evaluate the stability of objects, or changes to lighting
and shadowing, for instance, for designing lampshades (Fig. 1.3) through
estimations provided by pARam. The fabricated artifact (Fig. 1.4) can then be
placed in the context in which it was configured digitally. All aforementioned
aspects bridge the disconnect between the space where parameters are defined
(i.e., a design workstation), and the space where they become relevant and
influential (i.e., the space they are fabricated for and used in). To further
support users in choosing fitting parameters, pARam embeds domain knowledge
beyond constraints (e.g, ergonomics-related suggestions). All aforementioned
aspects are meant to increase the expressivity of in-situ design tools that
operate on existing designs, without demanding the complexity of highly
complex, industry-grade 3D-modeling tools.

Through a user study ($n=20$) that compared pARam with a desktop-bound
alternative, we found that users take their physical context into account
while designing, and engage in more spatial interactions revolving around
previewing and manipulating the model. Users appreciated alternative means of
defining parameter values, like using gestures to measure or draw curves. The
participants preferred the desktop modality for functional objects, and
preferred the AR variant for more aesthetic designs. However, the in-situ and
spatial nature of pARam led users to engage more in “eyeballing” (Mahapatra et
al., 2019) and approximations, which is appropriate for some, but not all,
design tasks in the context of personal fabrication.

Our work contributes the following:

  1. (1)

Presentation and discussion of the concept of in-situ interaction with
parametric designs, an emerging design approach for personal fabrication that
leverages extended reality and parametric designs to enhance the expressivity
of retrieval-oriented design tools.

  2. (2)

Design, development, and evaluation ($n=20$) of pARam, an in-situ design tool
to interact with parametric designs, enriched with input and validation steps
enabled by extended reality, embodying the aforementioned concept.

##  1\. Related Work

pARam is inspired by a set of overlapping research domains surrounding design
and fabrication: parametric design tools, design tools for personal
fabrication, and XR design tools. pARam is inspired by these works in several
ways: It embraces the notions of simpler, easier-to-use design tools, situates
relevant design effort in the usage context, and aims to embed domain
knowledge in the system, instead of expecting users to acquire it over time.

##### Definitions

The interaction with a parametric design can be viewed from two perspectives:
the creation (i.e., authoring) of parametric objects and their customization.
Mario Carpo describes this dichotomy as one “[…] where the primary author
designs a generic (parametric) object, and one or more secondary authors, or
interactors, adjust and adapt some variable aspects of the original notation
at will” (Carpo, 2017). This yields distinct roles of “modeling” and
“navigating” (e Costa et al., 2020). Notably, design through a parametric
model was described as a practice “where design variations are effortless”
(Barrios Hernandez, 2006). In the scope of this work, we treat the
“interaction with a parametric design” _not_ to be the act of authoring one
(e.g., a designer creating customizable furniture in a tool like
Grasshopper555https://www.rhino3d.com/features/#grasshopper, Accessed:
30.01.2024), but rather the _act of customizing such a design to one’s
requirements and liking_ (e.g., an end-user customizing one for their own
home). The lens of end-users is particularly relevant for the space of
personal fabrication, where users are handed the means to design and fabricate
unique artifacts, but may lack the necessary domain knowledge (Stemasov et
al., 2021; Baudisch and Mueller, 2017) to do so “from scratch”.

Design can further be implemented as either an _ex-situ_ or an _in-situ_
process. In this context of personal fabrication, Ashbrook et al. defined in-
situ design as a procedure where users design new artifacts “with in its
existing context” (Ashbrook et al., 2016). It is contrasted with in-situ
fabrication, where the manufacturing of an object happens in conjunction with
pre-existing ones (Ashbrook et al., 2016; Campos Zamora et al., 2022; Stemasov
et al., 2022a). pARam focuses on _in-situ design_ , given that industrial
fabrication capabilities may be easier to scale to unique, one-off designs in
the context of mass-personalization (Ozdemir et al., 22ed).

###  1.1. Parametric Design Tools

##### Interacting with Parametric Designs

Various approaches to parametric design are present in domains of architecture
or graphics, predating their use in personal fabrication (Anderl and Mendgen,
1995). Michel and Boubekeur presented a technical approach to enable direct,
brush-like interaction with parametric shapes, which is also an outstanding
example of an interactive technique for design (Michel and Boubekeur, 2021).
Buyruk and ??a??da?? presented a mixed reality approach for architectural
design that relies on robotic fabrication, introducing a framework unifying
physical and digital components using a digital twin approach (Buyruk and
Çağdaş, 2022). Design space exploration was probed for generative design
systems (Matejka et al., 2018), artificial intelligence systems (Urban Davis
et al., 2021), and situated in immersive environments (Jennings et al., 2022;
Lao et al., 2021).

The aforementioned works have informed our design choices on fundamental
interactions (e.g., sliders) but also considerations on validity (e.g.,
geometrically sound models). However, most approaches still separate design in
disjoint design and usage environments–an aspect we seek to bridge with pARam.

##### Customizers in Personal Fabrication

Personal fabrication (Baudisch and Mueller, 2017; Mota, 2011), in contrast to
industrial manufacturing processes or graphics contexts, follows a different
set of goals and constraints: not only are the outcomes physical, not digital,
but, most importantly, the tools are also routinely used by non-experts (Lee
et al., 2017). This has manifested in various prototype systems that provide
parametric design tools for the context of personal fabrication. To design
parametric designs themselves, tool support is similarly needed, for instance,
in defining constraints in a structure to achieve valid and adaptive designs.
This was addressed by Shugrina et al. with FabForms, an approach to ensuring
that the outcomes from a customizer are not only geometrically valid but also
manufacturable (Shugrina et al., 2015). This weaves domain knowledge into a
design or customization tool instead of expecting end-users to acquire and
apply it. The creation of constraint-based parametric designs was explored
through CODA by Veuskens et al., which provides tool support for constraint
specification (Veuskens et al., 2021). The “customizer” on Thingiverse
666https://www.thingiverse.com/app:22, Accessed: 29.01.2024 is a popular
example of an accessible parametric design configurator (Flath et al., 2017).
Its use has been studied extensively (Oehlberg et al., 2015; Voigt, 2018;
Acher et al., 2014)–while not impeccable, it, along with product customizers
in commerce, is a viable entry point for novices who may not want to engage in
prolonged learning processes (e.g., to learn 3D-modeling).

pARam is, fundamentally, a tool to _interact_ with a parametric design. The
approach of parametric designs is well established in engineering and
architecture (Caetano et al., 2020; Stals et al., 2021; Carpo, 2017).
Fundamentally, this approach constrains input and output spaces a user can
leverage to design an object, turning this design process into a
configuration. Based on this notion of “configuring” instead of “modeling”
(Stemasov et al., 2021), parametric design tools became represented in
everyday life in the form of various product configurators for consumer goods.
pARam aims to situate (i.e., in the context of use) and support (i.e., though
validations) the design process of non-experts while focusing on a _physical_
output domain, namely manufactured artifacts.

###  1.2. Design Tools for Personal Fabrication

##### Novice-Oriented Design Tools

_Design_ is often conflated with (3D)-modeling, especially in the context of
personal fabrication. However, other ways to acquire fitting designs are
present in research. Search and retrieval of models for fabrication has also
been discussed in previous works focusing on previewing and remixing (Stemasov
et al., 2020) or query formulation (Stemasov et al., 2022b) in-situ.
CustomizAR by Liang et al. is a tool to explore customizable designs on
Thingiverse, which further supported users with a tablet-based measurement
functionality (Liang et al., 2022). The work is closely related to our work
and pARam. Liang et al. support the measurement of relevant dimensions but
also the exploration of designs and further evaluate their camera-based
measurement approach, confirming that it may provide sufficient precision. The
authors further outlined how using established measurement tools may not
prevent errors (Liang et al., 2022), as measuring is a skill in and of its own
(Ramakers et al., 2023). We aim to further explore and augment in-situ
interactions with parametric designs, for instance, by providing
recommendations and early validations. A comparably influential work for pARam
is BodyMeter by Lee et al. (Lee et al., 2016). The authors explored how the
customization of furniture may be facilitated through spatial, embodied, and
gestural interaction instead of detailed definition through modeling software.
This exploration inspired both the pARam’s underlying concept and specific
features, like the gesture+voice-based measurement component.

All aforementioned works are relevant to pARam, as they aim to balance low
entry barriers with high, or at least sufficient-to-the-task expressivity.
They leverage other approaches than “modeling” (Stemasov et al., 2021), yet
allow users to find the designs they want or customize existing ones to their
liking.

The aforementioned considerations are also present in various tools that
enable the design for fabrication, while focusing on novice users, like Kyub
(Baudisch et al., 2019), FlatFitFab (McCrae et al., 2014), or application
bridges like Blocks To CAD (Lafreniere and Grossman, 2018). Works by Follmer
et al. presented ways to alter one’s physical environment (Follmer et al.,
2010) or make 3D-modeling accessible to children (Follmer and Ishii, 2012),
both of which leverage _remixing_ over _modeling_ – an approach we embrace
with pARam. SketchChair by Saul et al. provides a design environment for
chairs, which, similarly to pARam, supports users through simulation and
ergonomics components (Saul et al., 2011). pARam builds upon this premise in
several ways: by situating the design process in the context of future use
(in-situ), which avoids the need for “reference geometry” (Saul et al., 2011)
and providing input support for a larger class of result objects (Stemasov et
al., 2021), beyond chairs. Ergonomics are a consideration present in various
design tools, such as Body2Desk (Lee et al., 2018), which encapsulate domain
knowledge (Baudisch and Mueller, 2017) and support users in benefitting from
it, without having to acquire it. In contrast to pARam, the work of Lee et al.
focuses on desks as the core object (Lee et al., 2018), while pARam aims to be
an interface to parametric designs in general, along with a focus on
interactions between artifact and context (in-situ), in addition to
interactions between artifact and user (Lee et al., 2018).

##### In-Situ Design for Fabrication

In-situ design has emerged as a potential component of lower-effort design
tools for personal fabrication. RefAR by Wu and Cheng (Wu and Cheng, 2022)
leverages depth sensing of a HoloLens to support a user’s in-situ sketching
process with features of the environment. This allows replication of context
features for a new design (Wu and Cheng, 2022). MixFab by Weichel et al.
(Weichel et al., 2014) is a design environment enabling design in mixed
reality, where physical objects can be brought into the space to use as
features and references (Weichel et al., 2014). Printy3D is an in-situ design
tool that combines interactive projection and tangible building blocks,
focused on the design of electronics enclosures made accessible to novice end-
users–specifically children (Yung et al., 2018). Tangible elements and XR have
also been combined for headset-based design tools (Stemasov et al., 2023a).

pARam embraces the notion of in-situ design for (personal) fabrication by
situating the customization of parametric designs towards a user’s context-
driven needs.

##### In-Situ Fabrication

The notion of “in-situ” actions can likewise be applied to the act of
fabrication or manufacturing, where interacting objects found in a specific
context, are included in the fabrication process. This includes approaches
enabling mobile fabrication (Roumen et al., 2016; Campos Zamora et al., 2022),
portable fabrication (Peek and Moyer, 2017; Quitmeyer and Perner-Wilson,
2015), or wearable fabrication (Stemasov et al., 2022a), each of them enabling
in-situ fabrication activities. RoMA by Peng et al. (Peng et al., 2018) is an
example where design and fabrication happen in parallel, through a
collaboration between a human user and a robotic arm, enabling a turn-taking
process (Peng et al., 2018). FusePrint is a smaller-scale setup, where
physical objects are brought into a fabrication environment to replicate their
features in complementary objects (e.g., pens brought in to support the design
of a pen holder) (Zhu et al., 2016). In the context of architecture, IRoP by
Mitterberger et al. (Mitterberger et al., 2022) presents an approach to
plastering that translates in-situ motion of designers to in-situ fabricated
plastering (Mitterberger et al., 2022). The work of Jahn et al. is also
situated in the architecture context and outlines a platform to move from 2D
to 3D design processes and situate them in construction environments (Jahn et
al., 2018).

pARam focuses on design in context but benefits from aspects like in-situ
estimations to inform fabrication early on. This facilitates digital
iteration, instead of physical prototyping.

###  1.3. Design in and for Extended Reality

Extended Reality (XR) has also been both a tool and a target domain for
design. Conceptually, there is a sizeable overlap between design tools for
_digital environments_ (e.g., games) and design tools for _physical artifacts_
, both operating under the umbrella term of computer-aided design (CAD).

##### Immersive Design

Remixed Reality by Lindlbauer and Wilson presented a set of interactions to
alter a digitally acquired environment (Lindlbauer and Wilson, 2018). This
approach can be used to author altered versions of it–an outcome similarly
targeted by pARam, albeit through physical modifications. In an AR context,
WindowShaping by Huo et al. presented interactions to use real-world elements
for a design process (Huo et al., 2017) and Han et al. proposed methods to
adapt AR interfaces to existing, real-world objects (Han et al., 2023). This
approach is manifested in the estimation functions of pARam, where scanned
aspects (e.g., the environment mesh) are used to support early evaluations
and, by extension, design decisions. VRSketchIn provided users with a drawing
tablet in VR, to author VR environments in-situ (Drey et al., 2020), by
providing _support_ to sketch one’s designs. Concepts like “Paramersive
Design” (Drogemuller et al., 2023), similarly to pARam, aim to leverage
interaction paradigms known in architecture and enrich them with the means of
XR (Drogemuller et al., 2023).

##### Authoring XR Experiences

With immersive environments as the target, designers routinely engage in the
creation of multimedial arrangements (Stemasov et al., 2023b), which is a non-
trivial task, especially for novices. Nebeling and Speicher provide a meta-
perspective on design tools for extended reality, outlining several classes of
tools, mapped by their fidelity and the required skill (Nebeling and Speicher,
2018). Sketched Reality by Kaimoto et al. is a design approach that bridges
digital sketches and physical output by coupling both dimensions, with each
being able to affect the other (Kaimoto et al., 2022). This relationship has
also been echoed in the context of personal fabrication (Stemasov et al.,
2021), which highlights the conceptual similarity of “design-for-XR” and
“design-for-fabrication” (Dogan et al., 2022). Nebeling further highlights the
need for collaboration in content creation (Nebeling, 2022), which, in the
context of architectural design (Carpo, 2017) and personal fabrication (Flath
et al., 2017) may be mediated through the exchange and remixing of parametric
designs (Oehlberg et al., 2015). Customizer interfaces are a way to create
remixes (Flath et al., 2017), which was considered as a way to support
creative practitioners in authoring immersive experiences through _sampling_
(Stemasov et al., 2023b). Specific tools like SpatialProto (Müller et al.,
2021), ProcessAR (Chidambaram et al., 2021), ProtoAR (Nebeling et al., 2018),
VRCeption (Gruenefeld et al., 2022), or VRFromX (Ipsita et al., 2021) are
examples where XR is leveraged to provide an authoring environment for new,
immersive environments and applications.

With our work, we do not target digital environments for the designs users may
make, but rather physical ones, which demand transfers and understanding of
requirements across physical (_usage_) and digital (_design_) environments.

Several works rely on path design (Hutton Pospick and Suma Rosenberg, 2023) or
sketching as a way to define curves (Bae et al., 2009, 2008) or express design
intent (Igarashi et al., 1999; Camba et al., 2022), which is also a relevant
parameter type in pARam. This was also considered from an evaluation
perspective by surveying the state of the art of how sketch-based systems are
evaluated and highlighting the absence of standardized methods to do so
(Machuca et al., 2023). Barrera Machuca et al. further outline the _types of
sketches_ and _interactions_ used in systems. pARam’s curve drawing
functionality primarily focuses on letting users generate “conceptual
sketches” (Mayra Donaji Barrera Machuca et al., 2023) to infer more precise
geometry. Teddy by Igarashi et al. is considered a seminal interface for 3D
design (Igarashi et al., 1999) where a free-hand sketch is converted to a
numerical representation without burdening the user with a detailed definition
of it, going from rough expression to a finished design.

pARam deliberately provides ways to use coarse gestures to define curved
elements instead of requiring users to precisely manipulate control points
(Igarashi et al., 1999) or deal with 3D-representations.

##  2\. Concept and Design Rationale

Before presenting the implementation of pARam, we first outline the notion of
in-situ interaction with parametric designs.

###  2.1. Novice-Facing Configurators & Parameters

As a formative exploration, we initially examined how parametric designs are
presented to (end-)users through the lens of configurator tools. Configurator
interfaces enable “mass-customization” (Lee and Saakes, 2022; Nachtigall et
al., 2019) (i.e., highly personal designs at scale) and are _interfaces to
parametric designs_. In this work, we focus on users’ _interactions with
parametric designs_ , which are currently mediated through these _configurator
tools_ or so-called _customizers_.

##### Commercial Configurator Interfaces

Tylko is an example of commercial furniture
customization777https://tylko.com/, Accessed: 30.01.2024. For a sideboard,
users choose styles (which translate to entire geometric setups) and set
dimensions (width, depth, height). The width parameter is _continuous_ ,
whereas height and depth are _discretized_ to 10 and 4 levels, respectively.
Users can also alter colors based on a selection and choose whether they want
back panels (boolean). To support users in estimating sizes, dimensions can be
shown everywhere, a silhouette of a person is rendered next to the furniture,
and users can toggle the display of household items (e.g., books, a laptop) in
the respective depiction of the furniture. The jewelry configurator “Cell
Cycle”888https://n-e-r-v-o-u-s.com/cellCycle/, Accessed: 30.01.2024 by Nervous
System is a more sophisticated example: users not only define sizing and twist
parameters, but can also alter the Voronoi-like pattern through direct
manipulation to merge and subdivide cells or morph the entire geometry. As
with the other configurators, there are options to choose a base type (e.g.,
cuff, ring), or a material, and a direct display of the price is shown. In the
space of personal fabrication, Thingiverse offers a rich selection of
customizable designs (Liang et al., 2022; Oehlberg et al., 2015) that are
based on openSCAD. An example of such a design is “Stretchy Bracelet” by
emmett999https://www.thingiverse.com/thing:13505, Accessed: 28.01.2024 from
2011, which is among the most commonly made designs on Thingiverse.
Noticeably, the parameters in that design are labeled as r2, h, w, t, n, and
m, with some being integer numbers, and others floating-point. Nevertheless,
the over 250 times it was fabricated by other users exhibit a variety of sizes
and colors. A more recent yet similarly popular example is the “Customizable
drawer box” by gpvillamil101010https://www.thingiverse.com/thing:421886,
Accessed: 28.01.2024. Here, parameter names are more descriptive, like drawer
height, and more diverse: users can enter text as a parameter (message) to be
embossed or choose options between which object to generate (part). Here,
parameters are grouped into categories like “basic”, “pattern”, and “advanced”
to structure them further. A collection of vases by Ferjerez on Thingiverse
111111https://www.thingiverse.com/thing:2638924, Accessed: 29.01.2024 consists
of a single parametric design that allows users to choose from different
styles (shape) that define a base shape, and a set of parameters to alter the
vase’s surface (e.g., spikes, levels). The author further provides a set of 50
already finished designs for users to fabricate directly to allow users to
_choose_ , instead of customizing.

From the examples above, various parameter types can be extracted: dimensional
parameters related to extents, rotations, or positions, repetitions, choices
from options, feature toggles, and other examples (e.g., media inputs like
text or images). However, for most parameter types, there are underlying, more
abstract classifications: functional parameters (e.g., related to press-fit),
aesthetic parameters, body-related parameters, environment-related parameters,
or parameters relevant to fabricating a design. We do not consider this list
to be all-encompassing but rather an initial exploration of adopted customizer
tools. An underlying issue is the configurators’ ex-situ nature: they require
users to design a bracelet without the arm it will be worn on, a shelf without
the room it will be in, and without its future contents, thereby omitting the
consideration of all _physical interactions_ between design and context.

###  2.2. Concept: In-Situ Interaction with PDs

We consider the concept of in-situ interaction with parametric designs to be a
way to enhance the expressivity of interfaces focused on retrieving artifacts
(e.g., (Stemasov et al., 2020; Giunchi et al., 2021)), without demanding
complex modeling tools. Leveraging extended reality as a vehicle to customize
designs for personal fabrication may change how we treat inputs to such a
customizer or parametric design. We argue that in-situ interaction with
parametric designs may permit the creators of customizer systems to embed and
expose more domain knowledge (Baudisch and Mueller, 2017; Lee and Saakes,
2022) through the tools. This can happen through recommendations but also
early evaluation, all within the users’ unique physical contexts–knowledge
often unavailable to authors od customizer tools. Treating an XR headset as a
sensor platform, then, allows support tailored to users’ environments (e.g.,
by providing suggestions grounded in the device’s scene understanding
(Stemasov et al., 2022b)).

##### Target Audience

With pARam, we aim to support novices to design who may lack domain knowledge
but still have unique requirements (e.g., how they want their spaces, for
instance, at home, to look like). They may be able to express vague
requirements (Koch et al., 2020) (e.g., labels (Stemasov et al., 2022b)) to
find a correct PD, and define specifics in-situ later on by exploring and
iterating.

##### Target Expressivity and Objects

We further focus on the configuration of everyday household artifacts (e.g.,
furniture) with functional and aesthetic components. They may not necessarily
demand the degree of precision afforded by industry-grade design tools, but
exhibit room for personalization to benefit end-users. We further assume that
they are either manufacturable with current or emergent means of personal
fabrication or through providers capable of accommodating personalized
artifacts (e.g., a print service).

###  2.3. Design Rationale & Design Goals for pARam

Based on the previous considerations, assumptions, and prior art surrounding
personal fabrication (e.g., (Liang et al., 2022; Stemasov et al., 2022b; Lee
et al., 2016), we outline the following high-level design goals (DGs) for
pARam to embody the concept of in-situ interaction with parametric designs:
• DG1: Enable in-situ interaction with parametric designs for end-user
configured, manufacturable artifact designs. To do so, we leverage extended
reality as a vehicle to foster in-situ previewing, configuration, and
evaluation. This is inspired by prior art in configuration tools (Lee et al.,
2016), and, to a degree, research in measurement-to-design transfers (Ramakers
et al., 2023; Kim et al., 2017). Most importantly, this tries to tackle the
status quo of ex-situ interactions with parametric design configurators that
are meant for consumers, as outlined in section 2.1, while also increasing the
potential expressivity of search-based in-situ tools for personal fabrication
(e.g., (Stemasov et al., 2020)).
• DG2: Support users in choosing valid and fitting parameters with respect to
their unique physical contexts and requirements. While this is inherent to
some customizer interfaces, they often focus on geometrical validity (Michel
and Boubekeur, 2021) or fabricability (Shugrina et al., 2015) and less on the
practicality and feasibility of a fabricated outcome. Addressing this is
enabled and supported by the use of extended reality as a previewing and
sensor platform (Weichel et al., 2014). This also involves supporting the
transfer and conversion of context information to parameters, which may then
reduce errors with respect to how a design interacts with the physical
environment (Ashbrook et al., 2016; Mahapatra et al., 2019).
• DG3: Support users in understanding the effects and impact of their chosen
parametrization early on and with respect to their physical environment (i.e.,
context). This is linked to DG2, and aims to provide an early understanding of
the consequences (Leigh and Maes, 2015) of acquiring or fabricating a designed
object. In the context of personal fabrication, the most fundamental
“consequence” of a design or configuration activity is the physical
acquisition of an object, which resides in a real and physical environment and
_interacts_ with it (Stemasov et al., 2020). Context-specific support and
understanding can, therefore, facilitate easier and more successful
configuration processes by providing earlier previews without fabricated
prototypes.

We relied on these design goals to guide the development of pARam, but also
argue that their underlying principles apply to fabrication-oriented design
tools beyond parametric designs.

##  3\. pARam

The following sections outline the basic interaction flow for pARam, followed
by descriptions of the functionalities pARam provides to enable and support
the in-situ interaction with parametric designs.

Figure 2. Overview of the process and functionalities supported by pARam.
pARam enables in-situ interaction with parametric designs for personal
fabrication (a). Relying on extended reality headsets facilitates in-situ
previewing, but also input support like direct measurements (b) or estimation
components that rely on the scanned environment mesh (c). Our implementation
of pARam focuses on steps 2 to 6 primarily, but covers the entire pipeline.

###  3.1. Envisioned Interaction Process

Fig. 2 depicts a process of how users may customize parametric designs using
pARam. Users start by choosing an available design to customize. This choice
is informed by their requirements (e.g., the desire for a new piece of
furniture) and the context (i.e. where this furniture will be used later on).
Both aspects are highly personal and, in their combination, may be unique to
each user and situation (Lee et al., 2017). The user can then position the
design at the location of future use (i.e., where it will be put after
fabrication, DG1). For a shelf, for instance, this may be in an alcove or
niche the shelf is supposed to fill. The configuration process is supported
(Fig. 2b) through embedded domain knowledge (i.e., ergonomic suggestions for
body-related objects), in-situ measurements, or gesture-based input (e.g., for
curve definition, DG2). The configured object can be evaluated visually. By
leveraging the scanned environment mesh, estimations (Fig. 2.c) like the
effects of a design on lighting and shadowing or the object’s stability on the
surface can be previewed and estimated early (DG3). After this validation
step, users may choose to fabricate the object they configured, ultimately
adding it to the physical context in which they configured it. We consider
this fabrication step to be beyond the scope of the paper and deliberately
focus on context and user requirements in the design step of the personal
fabrication pipeline. Notably, the process differs from parametric design
tools established in personal fabrication (e.g., openSCAD) in two ways: 1) it
situates all design activities in-situ, and 2) it facilitates in-situ
iterations _prior to fabrication_ and aims to require no fabricated
iterations.

###  3.2. Implementation

pARam was implemented for the Microsoft HoloLens
2121212https://www.microsoft.com/en-us/hololens, Accessed: 25.01.2024 using
Unity 3D 2021.2.15. Two libraries were crucial for the implementation: MRTK
2.7.3131313https://github.com/microsoft/MixedRealityToolkit-Unity, Accessed:
25.01.2024, to handle inputs and interactions using the HoloLens, and
Archimatix,141414https://www.archimatix.com/, Accessed: 26.01.2024 to author
custom parametric models for the application. Archimatix was found to be
particularly suitable for development of pARam, as it is tightly integrated
with the game engine used, yet provides a rich and flexible environment to
_author_ parametric designs. Alternative approaches like 1) using a networked
instance of Grasshopper151515https://www.grasshopper3d.com/, Accessed:
30.01.2024, which would introduce latency and integration effort, or 2) pre-
generating all possible variations of a design (e.g., using openSCAD), which
would not scale, were considered, but ultimately discarded in favor of
Archimatix. In total, pARam provides 14 designs: 8 lampshades or vases, 2
tables, 3 shelf units, and a design for generic seating furniture (cf. Fig. 14
in the appendix). Additionally, pARam also included a deliberately simple
design (3 parameters only) for a book holder, used in the study (cf. section
4) for users to familiarize themselves with the tool.

Figure 3. Basic elements of pARam’s user interface. A 2D UI (a) provides
buttons to toggle all input and estimation modes and displays all parameters
with a name, a slider, and a value display. A 3D model preview (b) allows the
user to position the object in space and manipulate handles, depicted as blue
spheres, to control individual parameters directly.

####  3.2.1. Fundamental Parameter Interactions

The following interactions are heavily inspired by the “state of the art” in
product configurators and parametric design (section 2.1). They transfer
interactions to a mixed-reality environment to enable in-situ configuration
and previewing of designs. pARam provides 3 fundamental interactions with the
design: positioning, slider-based changes, and changes through direct
manipulation (DG1). An overview is depicted in Fig. 3, with the following
paragraphs elaborating on individual features and their implementation.

##### Positioning and Rotation

Users can initially interact with the most basic parameters of a parametric
design: the position and the orientation of the object (Fig. 3b), which, in
ex-situ configurators is deliberately omitted. This allows them to position
the digital preview at the location where they want to have the fabricated
result161616see also Fig. 15, appendix. This is enabled by a gizmo above the
model, provided by the MRTK package. Users can position it with three degrees
of freedom simultaneously and rotate it along the y-axis (Fig. 3b). This
constraint is appropriate for all objects provided through pARam as they are
objects with a single, appropriate up-direction. Scaling is not part of this
manipulation, as it is covered by the parameters of each model.

##### Sliders

As established in most expert and consumer-facing tools, sliders can be used
to alter numeric values. The limits of the slider convey basic constraints and
ranges for each parameter. The sliders are shown on a 2D user interface (UI),
seen in Fig. 3a. This allows users to control separate dimensions of a design,
reduced to a linear mapping171717see also Fig. 16, appendix. The effects of
the changes are reflected immediately in the positioned design. Users can
position the UI anywhere in the space (world-fixed), but can also recall it to
their hands immediately, by looking at their palm. The interface is then
attached to the respective hand and can be positioned in the space again.

##### Direct Manipulation

Users can also use direct manipulation to interact with the parameters using
handles (Fig. 4). This conveys the relationship between a parameter and its
effects on geometry more directly and avoids mediated interaction through a 2D
UI (Michel and Boubekeur, 2021; McElroy and Huang, 2023). To manipulate
parameters through the model, users can pinch any of the spheres positioned at
and around a model and start moving it. The model is updated accordingly and
at interactive speeds. Where fitting, the spheres also show arrows
representing an axis on which the parameter can be altered, as an additional
support mechanism. This can be seen in Fig. 4.2, where a radius is altered,
and the arrows point along the radial direction.

Figure 4. As an addition to slider-based interactions, pARam lets users
directly manipulate aspects of the model that are described by parameters, as
an alternative to the 2D UI (1). Users can grab the spheres to change
associated parameters (2). A direction hint is shown for parameters linked to
an axis (2, top right). After letting go, the respective parameter is updated
in the UI (3). Multidimensional parameters (e.g., positions), can be altered
in a similar fashion (4).

##### Constraints

In addition to the range constraints for each parameter being indicated in the
UI (i.e., the lowest and highest values of each slider), pARam also includes
interconnected constraints. Interconnected constraints describe valid ranges
of specific sliders in relation to others. For instance, a chair’s armrest
generally should not extend beyond the chair’s seating surface to ensure
connected geometry and, therefore, a degree of stability (Fig. 5.1).
Constraints like these ensure valid objects, as encoded by the person creating
the parametric designs. Whenever a user alters a parameter in an invalid
fashion, the model “snaps back” to a valid prior state (Fig. 5.2). Constraints
are set by the PD’s author and can be absolute (e.g., a maximal value) or
relative (e.g., a maximal value that changes depending on a different
parameter value). In Fig. 5, the maximum extent of the armrest depends on the
depth of the seating area, making it a relative constraint. This further
supports the notion of parametric designs, exclusively producing valid
outcomes if the design’s author sufficiently constrained the output space
(Shugrina et al., 2015).

Figure 5. For parameter manipulations, pARam allows users to temporarily
ignore validity constraints (1), where the armrest protrudes beyond the seat,
but restores a valid state of the model as soon as the user ends their
interaction (2), with the model visibly snapping back to a valid state.

####  3.2.2. Input Support

The second group of functions relates to DG1 (in-situ interaction) and DG2
(support for parameter choice). pARam leverages the capabilities of a mixed
reality headset to support users in previewing designs, but also in
configuring them.

##### Gesture-Based Measurement

pARam provides a simplified way to measure distances through gestures,
inspired by prior art in HCI (Lee et al., 2016, 2018). Given that most
parameters relate to measurements and dimensions, this aspect is crucial to
consider. Error-prone transfers between analog measurement devices and digital
design environments can either be bridged through connected measurement
devices (e.g., (Weichel et al., 2015)) or other support approaches (Yung et
al., 2018). Users can generate inputs to parameters through gesture-based
measurements in two ways: one-hand measurements and two-hand measurements.
One-handed measurements are taken between the user’s thumb and index finger on
one hand (Fig. 6a). Two-handed measurements are taken between the user’s index
fingers (Fig. 6b). Both gestures are reasonably intuitive and are used in
human-to-human communication of sizes and extents (Kopp, 2005; Lee et al.,
2016). Given that this feature may require both hands to operate, we settled
on voice input to capture the measurements fluidly (Fig. 6c). Users initially
point at a slider (on the UI) or handle (on the model) and say “measure this”
(Fig. 6c-1), which brings up a ruler between their fingers. They can then
measure relevant interacting objects (e.g., the circumference of a plant while
designing a pot). When they have settled on a length, they may say “take
measure” to record the value (Fig. 6c-2) and directly alter the chosen
parameter (Fig. 6c-3). Users may also use this functionality to get an
impression of size values in isolation or with respect to the context. The
feature is based on the open-source work of Hiromu
Kato181818https://github.com/HiromuKato/MRTK_HKSample, Accessed: 27.01.2024.
We observed a measurement error between approximately 0.2 and 1cm across the
tracking range. This was done informally by 2 of the authors repeatedly
testing both measurement gesture modes along a ruler or a tape measure, but is
roughly in line with results in literature (Soares et al., 2021; Guo and
Prabhakaran, 2022). While it may not achieve impeccable precision, this
interaction is likely valuable (Lee et al., 2018, 2016) and will become even
more useful given better tracking mechanisms. The measurement support allows
users to measure interacting objects in-situ and immediately transfer these
measurements to their design. This additionally allows users to take more
complex measures, for instance, around irregularly shaped objects without
employing calipers.

Figure 6. pARam provides 2 modes for gesture-based measuring: one-handed (a),
where the distance between index finger and thumb is used, and two-handed (b),
where the distance between index fingers is taken. This feature can be used as
a reference, or as a direct input to a parameter chosen previously. To leave
the hands free for such measurements, pARam provides voice interactions (c).
Users can first point at a parameter to change, say “measure this” (1), move
their fingers to the desired distance, and say “take measure” (2) to transfer
the current value to the previously chosen parameter (3)

##### Ergonomics Recommendations

pARam also aims to simplify the way inputs are made to designs related to
users’ bodies (i.e., ergonomics-related ones). Home artifacts interact with
users’ bodies differently across different dimensions (e.g.,
prolonged/briefly, different body parts), which has been woven into expert-
oriented (Lee et al., 2019; Lili et al., 2010) and novice-friendly (Saul et
al., 2011) design tools before. For seating-related objects (for pARam,
tables, and chairs or benches), pARam provides a way to configure a set of
users and their physical properties to receive recommended parameter ranges,
where applicable. While constraining the parameter space is inherent to
parametric designs, they often lack recommendations as to how to navigate this
constrained input space to achieve desirable results. Dealing with aspects
like ergonomics demands domain knowledge and often boils down to designing for
the majority (Eastman Kodak Company, 2011). A balance has to be struck between
expressivity (i.e., being able to generate a _valid_ design) and fit (i.e.,
arriving at a configuration that fulfills criteria not present in the design
environment). Users first toggle the creation of profiles for each future user
(Fig. 7.1-3). A bench to be used by a family would then require a profile for
each of the members. For each profile, users input aspects like height or body
composition. pARam then recommended ranges in the UI (Fig. 7.4), similarly to
prior approaches (Lee et al., 2018). The ranges are based on previous works
(Jocher and Loch, 2010; Openshaw and Taylor, 2006; Eastman Kodak Company,
2011), in our case primarily on the guide by Jocher and Loch (Jocher and Loch,
2010). Ergonomics is a highly complex field – through this function, parts of
the domain knowledge needed to make reasonable decisions are provided to users
through the interface without over-constraining the design.

Figure 7. To support the configuration of body-related objects (e.g.,
furniture), pARam allows users to configure a profile (1), which consists of a
number of users (2), and their individual physical composition (3). Based on
these profiles, recommendations for parameter ranges are shown on the slider
interface in orange (4). The ranges provide a viable starting point, and can
be based on one or several such profiles, reconciling parameter ranges across
a set of differently-sized future users.

##### Curves

To simplify the definition of objects with curvature-related parameters, pARam
allows users to sketch their desired silhouette in mid-air (Fig. 8). Curves
are generally complex to define, and parameters related to B??zier Curves
(consisting of points and tangents) lose their spatial nature when treated as
numerical parameters (Yin, 2023). Prior works have deliberately obscured this
representation (Igarashi et al., 1999), or focused on avoiding self-
intersecting results (Yuksel, 2020). For objects consisting of curvature-
related parameters, users can draw B??zier curves in mid-air to define how
curved objects may look like. When presented with a design that supports such
curvature parameters (e.g., lampshades or vases in pARam), users can toggle a
mode that allows them to draw their desired curvature (Fig. 8.1). A pinch
gesture lets pARam track the motion, visualized by sampled points on the path
(Fig. 8.2). The path points are projected into a plane orthogonal to the
user’s view direction to convert the curve into a format suitable for
Archimatix (i.e., one that operations like repeaters require). This 2D path is
automatically converted to one or multiple cubic B??zier curves, potentially
omitting details from the path, but also ensuring a smooth–“beautified” (Mayra
Donaji Barrera Machuca et al., 2023)–result (Fig. 8.3). Paths that ignore
constraints (e.g., a minimal size) or are impossible to approximate with the
design’s number of B??zier curves are possible to draw, but are converted to a
curve set that fulfills all constraints. Therefore, the result may differ from
the sketched curve but remains valid. To correct the result inferred from the
sketch, users can either re-draw the curve or correct it using the handles on
the model itself (Fig. 8.4).

Figure 8. pARam allows users to define curvature-related parameters using mid-
air gestures. Starting from a base design (1), users can draw their desired
silhouette in mid-air. Their input is visualized with a blue trace (2) and is
converted to a B??zier curve and applied to the model (3). After this action,
users can use this as a starting point for further parameter-based
manipulations (4).

####  3.2.3. Validation Support

Lastly, pARam aims to provide a set of features to support validating designs
with respect to the context they will be fabricated for. pARam leverages the
means of extended reality to support these context-dependent validations: the
scanned environment mesh is an input for both validation components, the light
estimation and the stability estimation. These estimation and validation
components are not meant to be impeccable, industry-grade simulations (which
are complex to set up and use), but rather quick and low-effort ways to
understand the effects of design decisions _in-context_ (DG3). pARam provides
2 sample components for that: a stability validation and a validation of
lighting and shadowing patterns, elaborated in the next paragraphs.

##### Stability Estimation

To evaluate the balance of an object, pARam provides a rudimentary validation
component for that (Fig. 9.1). The configured model is dropped from a small
height onto the environment (Fig. 9.2-4). Stable designs do not topple from
this motion, while top-heavy ones or designs with a small base surface do.
This allows users to get a glimpse of a design’s stability and balance and
react accordingly (Fig. 9.5-6). The feature relies on the scanned environment
mesh provided by the HoloLens and the plane detection mechanism. The collision
between the model and the surface is handled through the physics engine of
Unity and uses default “physics materials” (i.e., configurations of mass and
friction) and convex colliders for the model. While this may not be a precise
simulation (both due to the “physics materials” and colliders used), it can
serve as a lower bound of stability regardless: a design that proves to be
unstable at this point is likely to be unstable when fabricated.

Figure 9. To understand the steadiness of designs, pARam simulates a small
drop of the model onto the scanned environment mesh. Users first position
their model above its future position (1). It is then dropped onto this
surface (2-3). A toppling design may still be functional but is indicative of
an unsteady object. Users can then alter parameters, like widening an object’s
base (5), and re-try the estimation (6).

##### Lighting and Shadowing Estimation

pARam also allows users to preview the effects a design may locally have on
lighting and shadow patterns. This is particularly relevant for objects like
lampshades, but can also be used with any object that interferes with light
(e.g., new furniture obstructing existing light fixtures). Lighting is a
crucial design element in interior design contexts (Karlen et al., 2017; Ren
et al., 2023), and any newly fabricated object is likely to alter this element
at least to some degree. Furthermore, lighting is often non-trivial to
estimate early (Karlen et al., 2017), yet is a design element and component
that is both functional (i.e., lighting has to be sufficient to read, for
instance) and aesthetic (i.e., it has to be visually pleasing and potentially
intriguing) (Karlen et al., 2017). Similarly to the stability validation, this
feature likewise relies on the scanned environment mesh (Fig. 10.1). A custom
shader is applied to the environment mesh to display the shadow and light
patterns (Fig. 10.2). This preview is based on a point light but could be
expanded to other types of lighting if these relationships are encoded in the
parametric design itself. Rendering shadows on an AR headset such as the
HoloLens is non-trivial and requires bright light patterns and an off-black
display of the actual shadows. As with the aforementioned stability
estimation, this feature does not yield a perfect representation but rather an
estimation (Fig. 10.3). Nevertheless, this estimation provides early, in-situ
feedback on how a design works in a context when lighting is involved.

Figure 10. pARam provides a function to estimate lighting and shadowing. This
is relevant when designing lampshades and comparable objects. The shadowing
pattern is projected onto the environments (1). Note that the effects of the
shader are only visible on the device. This required a through-the-lens
photograph instead of a screenshot for (1). Game engine view (2) and
fabricated result (3) for reference.

##  4\. Evaluation

We chose to evaluate pARam and the underlying concept of in-situ interactions
with parametric designs through a user study.

Figure 11. The user study compared pARam with an analogous desktop version
(a), which was similarly enriched with estimation capabilities (a2) and
implemented following design patterns present in consumer-grade tools like
openSCAD or the Thingiverse customizer. A practice task, a bookholder with
only 3 parameters was used for participants to familiarize themselves with the
respective tools (a1–desktop, b–pARam)

We chose to compare pARam to a desktop-bound baseline-like application. This
was meant to allow us to further understand the underlying concept of in-situ
interaction with parametric designs, compared to what one may consider the
status quo: ex-situ interaction to find and tune parameters. The underlying
goal was to understand whether pARam, as a proof-of-concept implementation,
can transfer the benefits of parametric designs to XR while providing
meaningful support through the physical context. A core goal in developing
this custom baseline was to achieve feature-parity between desktop and AR
versions, meaning that added functions (e.g., a light estimation) should be
present in both versions. Nevertheless, the design of the desktop version
followed patterns and paradigms present in user-facing parametric designs,
such as openSCAD, the Thingiverse customizer, commercial services like Tylko,
or academic works like Fab Forms (Shugrina et al., 2015). Specifically, they
all follow an approach combining sliders, text fields, dropdowns, and a
preview, occasionally used for direct manipulation.

###  4.1. Sample

The sample for the evaluation consisted of 21 participants. One of them had to
drop out due to intermittent technical issues, leaving us with a sample of
$n=20$. 6 of the participants identified as female, one person identified as
non-binary, and 13 participants identified as male. The sample had a mean age
of 24.65 ($SD=2.11$), with their backgrounds being mostly students (e.g., in
finance, psychology, computer science). 11 participants had experience with VR
and AR, specifically using headsets like the Meta Quest or the HoloLens. Out
of these 11, 6 reported a prior usage duration of 1-10 hours, 3 reported
10-100 hours, and 2 reported over 100 hours of use. 13 participants reported
having used product configurators before (e.g., for cars, furniture, or
clothing, specifically footwear). Similarly, 13 participants reported having
used 3D-modeling tools before (e.g., AutoCAD, Blender, Tinkercad, SketchUp,
but also design tools like Unity3D), which cover “upper” classes of content
creation tools in the classification of Nebeling and Speicher (Nebeling and
Speicher, 2018). Out of these 13, 3 reported a prior usage duration of 1-10
hours, 5 reported 10-100 hours, and 5 reported over 100 hours of use. We
classify the 10 participants with over 10 hours of prior use as “experts” in
the following steps.

###  4.2. Procedure

The study was conducted in a meeting room of our institution. Various pieces
of furniture were present and were actively embedded in the task design (cf.
section 4.2). The participants were recruited around our institution with the
help of mailing lists. After a greeting and a brief introduction, participants
signed consent forms. They then received an introduction to the respective
tool they will be using (pARam or its desktop counterpart). A simple model
consisting of 3 parameters – a book-holder – was then used as a training
environment for the participants to familiarize themselves with the tool (Fig.
11). Participants then received instructions regarding the task they would be
solving. This information was also available as a printout for them to
reference on demand. After finishing the first task, users filled out a post-
task questionnaire. This was repeated for the second tool and concluded with a
final questionnaire comparing the tools and acquiring demographic data. The
study took 60 to 70 minutes. Participants received a reimbursement of 12€ for
their time.

##### Tasks

Participants were asked to solve 2 distinct tasks, with either pARam or the
desktop variant. The order and the tool-task combinations were counterbalanced
using a Latin square. The two tasks were the configuration of a bench and the
configuration of a lampshade. Each task consisted of 3 main instructions and a
bonus instruction. For the bench, participants were asked to design a bench
for themselves and another fictional person to use comfortably. The other
person was described as having an average physique and a height of 1.77
meters. Participants had to B1) design it in a way that it is as comfortable
as possible for them and the fictional partner, B2) the width of the bench
should not exceed the length of the table in the study room, B3) the armrest
height should be aligned with the height of the side table in the study room,
and optionally, B4) the bench should fit 2 seat cushions provided by the study
coordinator. For the lampshade task, users were asked to design a lampshade
that is visually pleasing in terms of shape and lighting/shadowing patterns.
The lampshade L1) should have a maximum height of 40cm, L2) its diameter must
not exceed the width of the bedside table in the study room, L3) be stable and
not topple easily, and optionally, L4) a provided candle should fit inside of
the lampshade. To solve the aforementioned tasks, participants had a set of
measurement tools freely available to them: calipers, a folding rule, a small
ruler, and a tape measure. Participants were also allowed to use them while
designing with pARam. All reference objects (e.g., the “bedside table”) were
easily accessible in the room and could be measured by the participants.

##### Measures

For the user study, we acquired the following measures: the System Usability
Scale (Brooke, 1996), the (raw) NASA Task Load Index (TLX) (Hart and
Staveland, 1988), and the Creativity Support Index (CSI) (Cherry and Latulipe,
2014). We further acquired a set of single-item Likert-scaled questions on
5-point scales and solicited usefulness rankings for each of the provided
features (cf. section 3). Participants were also asked to express modality
preferences (i.e., AR vs. Desktop) for specific design tasks. Successes and
failures concerning individual tasks were acquired following the criteria
outlined in section 4.2. Lastly, all interactions (i.e., features being used),
were logged to allow for an analysis of patterns and strategies participants
demonstrated.

###  4.3. Results

The following paragraphs outline the results we were able to gather from the
evaluation.

Figure 12. Overview over statement ratings and application preferences from
the user study: a) Participants’ stances towards the tool they used and
towards the outcome of their design process using the tool, rated on a 5-point
Likert scale; b) preferences towards applications (desktop and pARam) rated by
participants on a 5-point scale for different categories of objects.

##### Questionnaire Data

For the analysis of the SUS and CSI scores, as well as for the individual TLX
items, we apply the Aligned Rank Transform (ART) for nonparametric factorial
analyses (Wobbrock et al., 2011). The ART found a significant main effect of
the tool used on System Usability Score ($F({1},{18})={15.50}$, p$<$0.001)
(AR: M=46.58, SD=11.38, Desktop: M=57.38, SD=7.98). This can be traced back,
at least in part, to suboptimal interactions with the spatial interface,
especially the sliders on the 2D interface. This criticism was voiced during
the study itself and was also found in the comment fields in the
questionnaire. The overall (raw) TLX score exhibited expected differences (AR:
M=3.56, SD=0.90)(Desktop: M=3.00, SD=0.73). Our analysis focuses on individual
subscales. On the TLX subscales of Performance (AR: M=5.60, SD=1.47 Desktop:
M=6.20, SD=0.95), Frustration. (AR: M=2.90, SD=1.62, Desktop: M=1.90,
SD=0.85), Mental Demand (AR: M=3.60, SD=1.50, Desktop: M=3.05, SD=1.61), and
Temporal Demand (AR: M=2.75, SD=1.71, Desktop: M=2.00, SD=1.12) no significant
effects were found. The ART found a significant interaction effect of the tool
used $\times$ model type on TLX Effort ($F({1},{18})={11.73}$, p=0.003)
between AR (M=3.65, SD=1.35) and Desktop (M=3.15, SD=1.46). The ART further
found a significant main effect of the tool used on Physical Demand
($F({1},{18})={9.68}$, p=0.006) between AR (M=2.85, SD=1.57) and Desktop
(M=1.70, SD=1.22). In light of the inherently spatial and more embodied
interaction fostered through the headset and the gestural interaction is a
reasonable outcome.

For the Creativity Support index, the ART found no significant effects. The
score for pARam (M=54.13, SD=15.98) was comparable to the one for the desktop
(M=55.40, SD=13.94). This was also the case when comparing ratings by experts
(M=55.08, SD=14.06) compared to non-experts (M=54.19, SD=16.66).

For most single-item Likert-scaled questions (Fig. 12a), no significant
differences were found. However, the ART found a significant main effect of
the tool used on the responses to “I can now imagine how the designed
furniture will look in the room” ($F({1},{18})={25.21}$, p$<$0.001) between AR
(M=4.60, SD=0.60) and Desktop (M=3.15, SD=1.39). The ART also found a
significant main effect of the tool used on the question rating for “I took
the environment of the room into account for the design”
($F({1},{18})={24.46}$, p$<$0.001) between AR (M=4.30, SD=1.30) and Desktop
(M=2.75, SD=1.59).

##### Task Successes and Error Causes

Based on the tasks outlined earlier, we further conducted an analysis of
successes. Based on the main tasks given to each participant (L1-L3 and
B1-B3), 2 people failed (i.e., did not complete one or more of the tasks) the
lamp task on the desktop and 3 did so using pARam. For the bench task, 4 users
failed at one or more of the mandatory subtasks using the desktop variant and
6 failed using pARam. When including failures in the optional tasks (i.e., L4
and B4), 4 people failed the bench task using the desktop, and 6 failed with
pARam. For the lamp task, 4 people failed any subtask using the desktop
variant, whereas 7 did so using pARam. 3 expert users made errors on the
desktop, compared to 3 non-experts. With pARam, 5 expert users made errors,
compared to 4 non-experts. When considering overall error extents, pARam
(M=5.86, SD=5.33) and the desktop counterpart (M=5.33, SD=4.71) exhibit
comparable ranges. When considering individual tasks, the errors in the bench
task on the desktop (M=6.48, SD=5.43) are comparable to the bench task using
pARam (M=6.5, SD=6.35). The error extents for the lampshade task were lower,
yet remained comparable between pARam (M=4.59, SD=3.02) and desktop (M=3,04,
SD=2.63). All failures for the bench task stemmed from B3 (desktop: 4, pARam:
6), a constraint regarding the armrest’s height, where an error tolerance of
1cm in each direction was allowed. The issues for the lampshade task were more
spread out, with issues present in L1 – maximum height (desktop: 1, pARam: 1),
L2 – maximum diameter (desktop: 1, pARam: 2), and L4 – fit of a candle
(optional) (desktop: 2, pARam: 4).

##### Tool and Feature Preferences

When asked to rate which tool they would rather use for a specific type of
object on a 5-point scale, a set of trends became visible. They are depicted
in Fig. 12b. There were clear preferences for a desktop-based interaction for
complex objects ($desktop:{45}\%,pARam:{25}\%$), and objects that require
precise measures ($desktop:{70}\%,pARam:{10}\%$). No definitive trend was
visible for simple objects ($desktop:{45}\%,pARam:{40}\%$) or functional
objects ($desktop:{45}\%,pARam:{25}\%$). In turn, there was a trend towards
pARam for objects that do not need precise measurements
($desktop:{20}\%,pARam:{70}\%$), lighting-related ones
($desktop:{25}\%,pARam:{70}\%$), objects that interact with the body
($desktop:{16}\%,pARam:{63}\%$), decorative objects
($desktop:{10}\%,pARam:{75}\%$), and, rather clearly, objects meant for a
specific place ($desktop:{15}\%,pARam:{75}\%$). Users were also asked to rank
the tool’s individual features with respect to their helpfulness in solving
the task given to them. For pARam, the curve drawing functionality was rated
as most helpful ($M_{rank}$=1.6), followed by the handles ($M_{rank}$=2.22),
sliders ($M_{rank}$=2.65), the measurement functions ($M_{rank}$=2.69), the
ergonomics recommendations ($M_{rank}$=3.25), the stability estimation
($M_{rank}$=3.45), and the light estimation ($M_{rank}$=4.2). Comparable
patterns emerged for the desktop version. Here, however, the sliders were
generally ranked as most helpful ($M_{rank}$=1.6), followed by the handles
($M_{rank}$=1.85), the ergonomics feature ($M_{rank}$=1.83), the stability
estimation ($M_{rank}$=2.79), and the light estimation ($M_{rank}$=3.38). It
is intriguing to note that subjective helpfulness does not translate 1:1 to
actual feature use, as seen in Fig. 13, given that fundamental interactions
are likely indispensable but less impressive compared to more use-case-
specific ones.

Figure 13. Aggregated interaction sequences for pARam (a) and its desktop (b)
counterpart. The first column indicates the tool (desktop, pARam), the second
column refers to individual features, followed by a split between experts (✓)
and non-experts (x). The timeline in the fourth column depicts individual
interactions, with their distribution further highlighted through a violin
plot. The soft cutoff for the study is indicated with a dashed line at the
10-minute mark. The last column provides a count for each interaction in a
line for pARam (c) and desktop (d).

##### Interaction Sequences and Feature Use

All interactions with the tools were logged. An aggregation of interactions
across all participants can be seen in Fig. 13. A complete set of per-
participant timelines is found in Fig. 17 in the appendix. Between the desktop
version (Fig. 13b) and pARam (Fig. 13a), a set of trends is further visible:
Experts generally measured more and did so earlier in the process, and more
measurement activities may also indicate task success. Measurement activities
were also more scattered across the process in pARam, likely due to their
easy-to-access nature. In contrast, measurement often preceded most, if not
all, activities for the desktop variant: users tried to acquire all relevant
measures first and then alter their design under these constraints. The
estimation functions of pARam (light, balance) generally happened later in the
process, given that they are meant to _validate_ finished designs or design
iterations, but were used less often than expected. For both modalities, the
estimation functions were used more commonly by experts, and using them may
indicate success in related tasks (e.g., L3). Interaction counts can be seen
in Fig. 13c and Fig. 13d. Sliders were used more often on the desktop
($avg.={23.9}$) compared to the handle-based interactions ($avg.={13.9}$). In
contrast, AR seemed to have fostered more direct interaction through the
handles ($avg.={12.2}$), compared to the sliders on the 2D UI ($avg.={9.3}$).
This is linked to both the highly spatial nature of the interaction and
visualization, but also to the higher physical demand (cf. section 4.3).
Lastly, users were generally quicker to complete their tasks on the desktop.

##### Observations and Open Comments

We further solicited non-mandatory open comments about the positive and
negative aspects of the respective tools. In contrast to responses to
standardized questionnaires like the SUS or NASA TLX, participants were highly
enthusiastic about pARam and the underlying concept. They stated that pARam
let them “[…] iterate quickly and freely” (P10) (also voiced by P5, 15, 20),
or “[…] let me imagine how it would look like at that exact place” (P6). This
core principle underlying in-situ interaction with parametric designs was
further appreciated with respect to _validating designs_ : “[…] I like the
possibility to explore in a dynamic way how my decision affects the outcome of
the design” (P15), “It was quite easy to design a matching lamp because I
could compare virtual and real world objects in place” (P21), which was an
“[…] an immense help” (P19). It was further appreciated for being “[…] fun
[…]” (P10), which was also voiced by P12, 17, 20. The measurement support was
also appreciated: “[…] the measuring tool made meeting the requirements really
easy” (P19), (in addition to P13, 20) and so were the estimation functions
(P14, 21).

As a downside, participants mentioned the basic input handling, especially
related to the sliders on the 2D interface: the “Pinching gesture was very
hard to do correct” (P3), or “The use of the sliders was a bit difficult to
get the exact value I wanted” (P14). Criticisms related to the HoloLens’
inputs and MRTK’s handling of them were also mentioned by most participants in
some way (P1,3-5,8-12,14,16,18-21) and can be optimized through UI sizing,
positioning, and sensitivity. The “Usable area of the glasses [was] very
limited” (P3), referring to the limited field of view of the HoloLens. This
sentiment was echoed by other participants (P5, 6, 8, 17).

The desktop variant, in contrast, was praised for how well the interactions on
a 2D screen worked with the sliders: it “very easy to design the model using
the sliders” (P7). Without in-situ references, recommendations became more
relevant: “The ergonomics feature helped a lot to define required width and
height of the furniture” (P21). For the desktop variant, participants voiced
concerns regarding slider-parameter links more commonly (P4, 5, 19-21).
Participants further suggested features for the desktop variant that borrow
functions from pARam: “[…] overlap [the model] with a picture of the room […]
to see how it looks like in an environment” (P15). Comparable sentiments were
also present in other participants’ comments (P1, 3, 6, 11). As with pARam,
participants also criticized aspects like view controls (P8, 9, 12, 16, 19) or
discoverability (P10, 21).

For most participants, we observed a vastly more spatial and dynamic approach
to configuration with pARam compared to the desktop variant. Not only did they
design and preview in-situ, but they also fluidly moved between phases of
measuring, designing, and evaluating (Fig. 13a). Using the desktop tool,
measurement activities were more present earlier, and became rarer later on.
An intriguing observation was that a small set of participants used the
provided analog measurement tools with pARam, prior to remembering its built-
in measurement functionalities and fully switching to use them instead.

##  5\. Discussion

The following sections engage with the study results and reflect on the
concept of in-situ interaction with parametric designs.

###  5.1. Insights from the Evaluation

From the user study, we gathered that pARam, as a proof of concept, is not
only functional and valid support for users, but also identified challenges
underlying notions of low-effort–high-expressivity design tools.

##### Spatial Interactions to Define Artifacts

The concept of in-situ interaction with parametric designs along with pARam
embraces the spatial nature of artifacts, but also the interactions to define
them. Participants praised this nature: not only did they actively move around
their design, but they also altered the position of the design freely, for
instance, to make it easier to fulfill environment-related design tasks (e.g.,
B2, B3, L2). An often-ignored parameter in various configurators (by their ex-
situ nature) is an artifact’s position in the user’s (and, therefore, the
designer’s) context of use. Embedding this parameter meaningfully in a design
tool either requires full and impeccable acquisition of that context, or
through situated interaction, as enabled by pARam. Interacting through
gestures is likely a highly approachable way to define objects (Holz and
Wilson, 2011; Lee et al., 2016) and can be further enriched with modalities
like speech (Ballagas et al., 2019). Spatial interactions, however, come at
the expense of precision and fidelity, at least with the current state-of-the-
art of tracking, which has to be considered upfront. The tradeoff between
achievable precision and spatial interaction is reflected in several
questionnaire items, like the TLX score, individual items, or the SUS score.
It may require surface-supported interactions, as shown with DesignAR
(Reipschläger and Dachselt, 2019). The comparable means in the CSI score may
indicate that pARam, as a creativity support tool, may be on par with the
desktop counterpart, but this claim warrants further studies.

##### Overconfidence through Gestural Measurements

The measurement precision and fidelity of users using pARam was arguably below
the one achieved with the desktop variant. Users were free to use the provided
measurement tools, but most chose to rely on pARam and gestural measurement.
This is likely grounded in the fact that transferring the acquired dimension
(Weichel et al., 2015) to the design tool (or even a specific parameter) was
made deliberately frictionless. A user’s fingers are, especially at small
scale and low margins for error, no replacement for a caliper. However, with
mechanisms to compensate for that (e.g., snapping (Nuernberger et al., 2016),
which takes context into account), they may approach a useful degree of
precision. While this is a technological limitation that can be rectified
through better tracking and compensation algorithms, there is a tendency
towards “eyeballing” or “guesstimating” when engaged in in-situ design. This
notion of coarse measurements may be sufficient for isolated artifacts and
will greatly reduce effort, but does not transfer to highly precise objects
that functionally interact with their environment. Mahapatra et. al identified
such issues (i.e., “eyeballing”) in their work (Mahapatra et al., 2019), and
we believe that direct visual feedback may reinforce those. Immediate, in-situ
visualization of the future artifacts seems to create a tendency to operate on
the visual preview, instead of precise and detailed measurements. Users may
then treat this preview as the _only_ feature to evaluate a design, unaware of
imprecisions in the model, the visualization, or their estimations. We
consider this tendency towards “eyeballing” and subsequent errors to be the
“cost of the system”, which, in future iterations have to be corrected, for
instance, by relating gestures to machine-measurable objects in the context
(Liang et al., 2022) or snapping (Nuernberger et al., 2016). Alternatively,
approaches like the ones presented by Kim et al. (Kim et al., 2017) or
Ramakers et al. (Ramakers et al., 2023) that allow for tolerant designs may
also support imprecise, low-effort design processes while retaining functional
outcomes.

##### Correspondences between Interface and Model

In some of the designs in pARam, there were a few parameters that were only
controllable through handles, and some were only controllable through the
sliders of the UI. This led to perceptions of unintuitiveness, where
participants were “[…] not completely sure what each of the blue spheres were
doing” (P14). Subsequently, they engaged in exploring and discovering
individual elements to understand their effects and constraints. Having both
the model and the UI in view–which is non-trivial for users to do with the
HoloLens–helped make logical connections, but highlighting and linking would
likely be helpful in future iterations of pARam.

###  5.2. Reflecting on In-Situ Interaction with PDs

##### Interactions for Working Across Scales

With pARam, both human-scale (Kovacs, 2021) or furniture-scale objects
(Agrawal et al., 2015) (e.g., tables, shelves) and smaller trinkets (e.g.,
vases, lampshades) are customized through the same interaction techniques at
different scales. While this helps, consistency, precision, and error
tolerances are likely to change between these scales. This likely requires a
more thorough consideration of gesture-based measurement, for instance,
incorporating wider gestures, and combining pointing with other modalities
(Lee et al., 2016). XR devices further can provide immediate, visual, and in-
situ feedback on such inputs. However, if these means for measurement are not
given (or are not precise enough), users will likely revert to in-situ
estimating and “eyeballing”, which may work in some, but not all cases.

##### Encoding and Evaluating Function Early

Any fabricated artifact fulfills–ideally–user requirements in terms of form
and function. Users can choose to emphasize specific aspects of those, but
they remain tightly coupled, given that they are designed for physical space,
not only a digital one. Encoding function and physical validity (Shugrina et
al., 2015) is non-trivial. In the study, we noticed this with the optional
candle task (L4), where users routinely emphasized form over this function,
given that the artifact was one that is inherently associated with looks.
Support for these tasks is crucial and is part of in-situ interaction with
parametric designs. While pARam attempted to provide this support, users had
to actively choose to use and interpret it correctly (e.g., what to do when
the stability estimation indicates a toppling design). For the ergonomics
recommendations, this was not the case for all the participants (Fig. 13), nor
was this the case for the light and stability estimations. This is an argument
for automated validations, which can understand design intent in isolation
(Hofmann et al., 2018), but also in relation to the context.

##### Bridging Physical Context and Digital Parameters

In-situ design allows for an overlap of usage context and design environment
(Yung et al., 2018; Weichel et al., 2014; Stemasov et al., 2020), but does not
free the user from engaging in some, potentially error-prone (Mahapatra et
al., 2019) transfers. The measurement component in pARam supports these
transfers, but does not guarantee that the dimension or context object being
measured by the user is correct, which was reflected in the error rates of
pARam. In turn, the previewing functionality enables the transfer from the
design environment to the context, without fabricating a prototype. While this
serves as a preview and a coarse validation, it does not provide a holistic
model of all interactions between the object and the context, which would
require a maximum degree of fabrication-awareness and scene understanding.
While this is not provided in our proof-of-concept implementation of pARam
yet, it is likely to be feasible with future extended reality platforms,
which, by design, rely on scene understanding to function.

##  6\. Limitations

Our research exhibits a set of limitations, which we discuss in the following
paragraphs.

##### Pipeline

The pipeline in pARam (cf. Fig. 2) assumes that the fitting customizable
object has already been found. However, for most users, the process starts
with searching objects, which is a non-trivial task and has been considered in
works like CustomizAR (Liang et al., 2022) or ShapeFindAR (Stemasov et al.,
2022b). pARam’s pipeline also ends before the actual acquisition step, which
we deemed to be optimizable without user interference (e.g., considering 3D
print services over fabrication at home). Nevertheless, we completed every
step of the process from design configuration to fabrication, outlined in Fig.
2, ourselves several times. This includes multiple 3D printed lampshades
(e.g., Fig. 1.4, top and Fig. 10) and a CNC-routed wooden shelf (Fig. 1.4,
bottom). We consider this to be an instance of “Evaluation by Demonstration”
(Ledo et al., 2018), albeit a compact one. The lampshades were printed using
an Ultimaker 2+ 3D
printer191919https://ultimaker.com/3d-printers/s-series/ultimaker-2-connect/,
Accessed: 24.01.2024, and the shelf was milled using a Shaper
Origin202020https://www.shapertools.com/en-us/origin, Accessed: 25.01.2024.
Both required the use of CAM212121For the lampshades: Ultimaker Cura
(https://ultimaker.com/software/ultimaker-cura/, Accessed: 24.01.2024), and
Slicer for Fusion 360
(https://www.autodesk.com/support/technical/article/caas/tsarticles/ts/3yg7zznS94MHNDG7KMV8Qg.html,
Accessed: 25.01.2024) for the shelf. (computer-aided manufacturing) software,
which could be integrated into the tool, as demonstrated by works like
Mix&Match (Stemasov et al., 2020), which included a slicer. Including this
step in future evaluations is promising for understanding the breakdowns in
transferring a user’s design to a manufactured artifact.

##### Object Set

pARam and, even more so, the study, focused on a limited set of designs to
customize. While they cover a broad range of scenarios and parameter types, an
even richer set of parametric designs may uncover different interaction
patterns or the need for other validation functionalities. This particularly
applies to artifact classes beyond established machines for personal
fabrication (i.e., 3D-Printers, CNC mills, laser cutters). For example,
parameters for clothing are likely more complex to measure and incorporate in
a design (Saakes et al., 2015; Wibowo et al., 2012), as they are potentially
more organic and irregular, yet uniquely personal.

##### Authoring

As outlined in section 1, our focus lies on the _customization_ of parametric
design through end-users. However, there is also demand for such designs to be
_created_ and _authored_ by expert designers (Carpo, 2017; Flath et al., 2017)
to enable customization in the first place (i.e., through meta-design (Fischer
and Scharff, 2000)). More complex environments (e.g., Archimatix or
Grasshopper) are appropriate here, and could benefit from in-situ approaches
nonetheless. With approaches like pARam, authoring customizable designs ought
to change in parallel, as designers have to encode more aspects in their
parametric design than before (i.e., more than static geometry). For pARam,
this includes all relations to ergonomic properties (e.g., what influences the
seating area or where an arm may rest), or individual handles, their
positions, and their direction indicators (cf. Fig. 4.2). This can also
include configuring multiple lights instead of a point light only, or
including other simulation domains, like fluids or aerodynamics, and encoding
their relationship with the context accordingly.

##### Estimations $\neq$ Simulations

We consciously label the evaluation functions of pARam as “estimations”, not
“simulations”. They require little configuration by the user and deliberately
do not provide a high fidelity. Nevertheless, we consider the results of the
estimations (balance, lighting) to be a way of early, worst-case evaluation
(Stemasov et al., 2023a), which, unlike others, takes into account the context
(e.g., a surface an object will rest on). With increasingly detailed scene
understanding coupled with a high degree of fabrication awareness, these
estimations will likely increase in value without added complexity for the end
users.

##### Study Design and Implementation Platform

Despite both consisting of subtasks that cover both ends of the spectrum
between form and function, participants did not perceive the tasks as
similarly complex. While fundamental differences and benefits between pARam
and its desktop counterpart were confirmed, further studies to understand
pARam as a design- and creativity-support-tool are needed, ideally covering
the entire pipeline from design choice to fabrication. Furthermore, despite
our goal to achieve full feature-parity between pARam and the desktop variant,
the curve drawing feature was absent from the desktop, which may explain the
high usefulness rankings (section 4.3). Most importantly, the ergonomic issues
(e.g., field of view, interaction precision) were not ideal and likely
influenced participants’ assessments. This also applies to the fidelity of the
environment scan that informed the light and balance estimations.

##### Interface and Underlying Constraints

The fundamental elements of the UI can also be expanded, as both the
interactions and the gizmos used (e.g., handles for direct manipulation or
sliders on the UI) were deliberately simple. While this worked well for the
user study, there is room for improvement and more fine-grained explorations
(cf. (Drey et al., 2020; Reipschläger and Dachselt, 2019; Kim et al., 2005)).
This also applies to the detailed implementation of constraints, where, for
example, an invalid curve yields a valid result that may not resemble the
user’s input closely.

Despite these limitations, we are confident that the in-situ interaction with
parametric design, as demonstrated by pARam, is a meaningful and highly
promising augmentation of how we currently use parametric designs in the
context of personal fabrication.

##  7\. Future Work

pARam opens up several avenues for future work, related to added
functionalities for pARam, further user studies, but also to the concept of
in-situ interaction with parametric designs itself.

##### Richer Model Set

Similarly to prior art (e.g., (Shugrina et al., 2015)), pARam operates on a
limited set of pre-designed models (cf. Fig. 14) that can certainly be
expanded. This would require feasible search functionalities (Büschel et al.,
2018; Stemasov et al., 2022b). This richer set of models likely opens up new
opportunities for interaction, like using a 3D scan – afforded by the use of
an XR headset (Stemasov et al., 2020) – as a higher-level input to a
parametric design. An early prototype of pARam also used OpenPose to
automatically acquire ergonomics-related body measures – similarly to Kim et
al. (Kim et al., 2021) – but was found to be unreliable and hard to reconcile
with a headset as a platform. The limitations in section 6 can also inform
future directions, such as improving feedback and transparency regarding
constraints and the logic they may follow to the user, as the current
implementation of pARam focused on exploring individual interaction
techniques.

##### Generalizing beyond Fabrication

In line with expanding the model set would be the consideration of domains
beyond (personal) fabrication. Physical context and interactions with this
environment are crucial to consider prior to _fabrication_ , but can also
inform purely digital domains where parametric designs can be relevant (e.g.,
digital design (Michel and Boubekeur, 2021)). Here, understanding requirements
of non-novices and scalability to more complex designs pose highly intriguing
challenges.

##### Automation and Evaluations

We also aim to further explore automation in this context, to further benefit
from these more abstract expressions of parameters: one may consider users
starting with vague gestural (Lee et al., 2016; Holz and Wilson, 2011),
spatial (Stemasov et al., 2022b), textual, or multimodal (Fraser et al., 2020)
descriptions of requirements to receive fitting results from a customizable
design. The estimation components can also be improved (i.e., made more
precise) and expanded, through approaches like fluid simulations (e.g., for
the design of hydroponics systems (Pearce, 2015; Takeuchi, 2019)), or load-
bearing simulations (Abdullah et al., 2021), to further focus on functional
mechanical parts. Balancing this degree of early evaluation with the tool’s
complexity is non-trivial and additionally demands a higher degree of
_fabrication-awareness_ (e.g., how dense the used material is). Lastly, we
plan user studies to understand usage patterns further and optimize for
interface ergonomics.

##  8\. Conclusion

Finding and starting with a finished design is a highly beneficial approach to
3D-modeling, but is limited in expressivity. Parametric designs lower the
effort needed to engage in 3D-design by replacing detailed modeling with
configuration. This notion is usually ex-situ and currently benefits domain
experts (e.g., architects), or enthusiasts configuring select 3D models on
repositories like Thingiverse.

To augment in-situ search-and-fabricate approaches and further simplify
interactions with parametric designs, we presented the concept of in-situ
interaction with parametric designs and introduced pARam, a proof-of-concept
implementation embodying this notion. pARam leverages extended reality to 1)
support users in choosing parameter values (e.g., through gesture-based inputs
or recommendations), and 2) provide early, in-context estimation
functionalities (e.g., lighting). We evaluated pARam in a user study and found
that it allows users to incorporate context into their design process while
also benefiting from in-situ estimations.

We argue that extended reality, as an emerging technology, is a crucial
support component to design for (personal) fabrication, as it enables not only
in-situ interaction, but also provides context understanding that can be woven
into design tools themselves, as demonstrated with pARam. This opens up a rich
space for design support, relevant for parametric designs and other approaches
to 3D-design by bridging unique user requirements, their unique contexts, and
their representations in design tools. Leveraging this combination may make
design for personal fabrication even more accessible to end-users, without
demanding physical design iterations or complex information transfers.

###### Acknowledgements.

We thank the participants of our user study for their time and the reviewers
of the manuscript for their thoughtful and helpful feedback.
This research was partially funded by the Deutsche Forschungsgemeinschaft
(DFG, German Research Foundation) through the project “Democratic and
Sustainable Personal Design and Fabrication through In-situ Co-Design and
Previsualization” (Project number: 525038300).
This work has been co-funded by the LOEWE initiative (Hesse, Germany) within
the emergenCITY center.

## References

  * (1)
  * Abdullah et al. (2021) Muhammad Abdullah, Martin Taraz, Yannis Kommana, Shohei Katakura, Robert Kovacs, Jotaro Shigeyama, Thijs Roumen, and Patrick Baudisch. 2021\.  FastForce: Real-Time Reinforcement of Laser-Cut Structures. In _Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems_ _(CHI ’21)_. Association for Computing Machinery, New York, NY, USA, 1–12.  https://doi.org/10.1145/3411764.3445466
  * Acher et al. (2014) Mathieu Acher, Benoit Baudry, Olivier Barais, and Jean-Marc Jézéquel. 2014.  Customization and 3D Printing: A Challenging Playground for Software Product Lines. In _Proceedings of the 18th International Software Product Line Conference - Volume 1_ _(SPLC ’14)_. ACM, New York, NY, USA, 142–146.  https://doi.org/10.1145/2648511.2648526
  * Agrawal et al. (2015) Harshit Agrawal, Udayan Umapathi, Robert Kovacs, Johannes Frohnhofen, Hsiang-Ting Chen, Stefanie Mueller, and Patrick Baudisch. 2015\.  Protopiper: Physically Sketching Room-Sized Objects at Actual Scale. In _Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology_ _(UIST ’15)_. ACM, New York, NY, USA, 427–436.  https://doi.org/10.1145/2807442.2807505
  * Alcock et al. (2016) Celena Alcock, Nathaniel Hudson, and Parmit K. Chilana. 2016\.  Barriers to Using, Customizing, and Printing 3D Designs on Thingiverse. In _Proceedings of the 19th International Conference on Supporting Group Work_ _(GROUP ’16)_. ACM, New York, NY, USA, 195–199.  https://doi.org/10.1145/2957276.2957301
  * Anderl and Mendgen (1995) R. Anderl and R. Mendgen. 1995.  Parametric Design and Its Impact on Solid Modeling Applications. In _Proceedings of the Third ACM Symposium on Solid Modeling and Applications_. ACM, Salt Lake City Utah USA, 1–12.  https://doi.org/10.1145/218013.218018
  * Ashbrook et al. (2016) Daniel Ashbrook, Shitao Stan Guo, and Alan Lambie. 2016\.  Towards Augmented Fabrication: Combining Fabricated and Existing Objects. In _Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems_ _(CHI EA ’16)_. ACM, New York, NY, USA, 1510–1518.  https://doi.org/10.1145/2851581.2892509
  * Bae et al. (2008) Seok-Hyung Bae, Ravin Balakrishnan, and Karan Singh. 2008\.  ILoveSketch: As-Natural-as-Possible Sketching System for Creating 3d Curve Models. In _Proceedings of the 21st Annual ACM Symposium on User Interface Software and Technology_ _(UIST ’08)_. Association for Computing Machinery, New York, NY, USA, 151–160.  https://doi.org/10.1145/1449715.1449740
  * Bae et al. (2009) Seok-Hyung Bae, Ravin Balakrishnan, and Karan Singh. 2009\.  EverybodyLovesSketch: 3D Sketching for a Broader Audience. In _Proceedings of the 22Nd Annual ACM Symposium on User Interface Software and Technology_ _(UIST ’09)_. ACM, New York, NY, USA, 59–68.  https://doi.org/10.1145/1622176.1622189
  * Ballagas et al. (2019) Rafael Ballagas, Jishang Wei, Mithra Vankipuram, Zhiyuan Li, Keanu Spies, and Hiroshi Horii. 2019\.  Exploring Pervasive Making Using Generative Modeling and Speech Input.  _IEEE Pervasive Computing_ 18, 4 (Oct. 2019), 20–28.  https://doi.org/10.1109/MPRV.2019.2929130
  * Barrios Hernandez (2006) Carlos Roberto Barrios Hernandez. 2006\.  Thinking Parametric Design: Introducing Parametric Gaudi.  _Design Studies_ 27, 3 (May 2006), 309–324.  https://doi.org/10.1016/j.destud.2005.11.006
  * Baudisch and Mueller (2017) Patrick Baudisch and Stefanie Mueller. 2017.  Personal Fabrication.  _Foundations and Trends® in Human–Computer Interaction_ 10, 3–4 (May 2017), 165–293.  https://doi.org/10.1561/1100000055
  * Baudisch et al. (2019) Patrick Baudisch, Arthur Silber, Yannis Kommana, Milan Gruner, Ludwig Wall, Kevin Reuss, Lukas Heilman, Robert Kovacs, Daniel Rechlitz, and Thijs Roumen. 2019\.  Kyub: A 3D Editor for Modeling Sturdy Laser-Cut Objects. In _Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems_ _(CHI ’19)_. Association for Computing Machinery, Glasgow, Scotland Uk, 1–12.  https://doi.org/10.1145/3290605.3300796
  * Brooke (1996) John Brooke. 1996\.  SUS-A Quick and Dirty Usability Scale.  _Usability evaluation in industry_ 189, 194 (1996), 4–7.
  * Büschel et al. (2018) Wolfgang Büschel, Annett Mitschick, and Raimund Dachselt. 2018.  Here and Now: Reality-Based Information Retrieval: Perspective Paper. In _Proceedings of the 2018 Conference on Human Information Interaction & Retrieval_ _(CHIIR ’18)_. Association for Computing Machinery, New York, NY, USA, 171–180.  https://doi.org/10.1145/3176349.3176384
  * Buyruk and Çağdaş (2022) Yusuf Buyruk and Gülen Çağdaş. 2022\.  Interactive Parametric Design and Robotic Fabrication within Mixed Reality Environment.  _Applied Sciences_ 12, 24 (Jan. 2022), 12797\.  https://doi.org/10.3390/app122412797
  * Caetano et al. (2020) Inês Caetano, Luís Santos, and António Leitão. 2020\.  Computational Design in Architecture: Defining Parametric, Generative, and Algorithmic Design.  _Frontiers of Architectural Research_ 9, 2 (June 2020), 287–300.  https://doi.org/10.1016/j.foar.2019.12.008
  * Camba et al. (2022) Jorge D. Camba, Pedro Company, and Ferran Naya. 2022\.  Sketch-Based Modeling in Mechanical Engineering Design: Current Status and Opportunities.  _Computer-Aided Design_ 150 (Sept. 2022), 103283\.  https://doi.org/10.1016/j.cad.2022.103283
  * Campos Zamora et al. (2022) Daniel Campos Zamora, Liang He, Yueqian Zhang, Xuhai Xu, Jennifer Mankoff, and Jon E. Froehlich. 2022.  sPrintr: Towards In-Situ Personal Fabrication Using a Mobile 3D Printer. In _Symposium on Computational Fabrication_ _(SCF ’22)_. Association for Computing Machinery, New York, NY, USA, 1–3.  https://doi.org/10.1145/3559400.3565587
  * Carpo (2017) Mario Carpo. 2017\.  _The Second Digital Turn: Design Beyond Intelligence_.  MIT Press, Cambridge, MA.  https://mitpress.mit.edu/9780262534024/the-second-digital-turn/
  * Cherry and Latulipe (2014) Erin Cherry and Celine Latulipe. 2014.  Quantifying the Creativity Support of Digital Tools through the Creativity Support Index.  _ACM Transactions on Computer-Human Interaction_ 21, 4 (June 2014), 21:1–21:25.  https://doi.org/10.1145/2617588
  * Chidambaram et al. (2021) Subramanian Chidambaram, Hank Huang, Fengming He, Xun Qian, Ana M Villanueva, Thomas S Redick, Wolfgang Stuerzlinger, and Karthik Ramani. 2021.  ProcessAR: An Augmented Reality-Based Tool to Create in-Situ Procedural 2D/3D AR Instructions. In _Designing Interactive Systems Conference 2021_. ACM, Virtual Event USA, 234–249.  https://doi.org/10.1145/3461778.3462126
  * Dogan et al. (2022) Mustafa Doga Dogan, Patrick Baudisch, Hrvoje Benko, Michael Nebeling, Huaishu Peng, Valkyrie Savage, and Stefanie Mueller. 2022\.  Fabricate It or Render It? Digital Fabrication vs. Virtual Reality for Creating Objects Instantly. In _Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems_ _(CHI EA ’22)_. Association for Computing Machinery, New York, NY, USA, 1–4.  https://doi.org/10.1145/3491101.3516510
  * Drey et al. (2020) Tobias Drey, Jan Gugenheimer, Julian Karlbauer, Maximilian Milo, and Enrico Rukzio. 2020\.  VRSketchIn: Exploring the Design Space of Pen and Tablet Interaction for 3D Sketching in Virtual Reality. In _Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems_. ACM.  https://doi.org/10.1145/3313831.3376628
  * Drogemuller et al. (2023) Adam Drogemuller, Hamidreza Sakhaei, Andrew Cunningham, Rongrong Yu, Ning Gu, and Bruce H. Thomas. 2023.  Envisioning Paramersive Design: An Immersive Approach to Architectural Design and Review. In _2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)_. 261–265.  https://doi.org/10.1109/ISMAR-Adjunct60411.2023.00060
  * e Costa et al. (2020) Eduardo Castro e Costa, Joaquim Jorge, Aaron D. Knochel, and José Pinto Duarte. 2020.  Enabling Parametric Design Space Exploration by Non-Designers.  _AI EDAM_ 34, 2 (May 2020), 160–175.  https://doi.org/10.1017/S0890060420000177
  * Eastman Kodak Company (2011) Eastman Kodak Company. 2011\.  _Kodak’s Ergonomic Design for People at Work_.  Wiley, Hoboken, NJ.
  * Fischer and Scharff (2000) Gerhard Fischer and Eric Scharff. 2000.  Meta-Design: Design for Designers. In _Proceedings of the 3rd Conference on Designing Interactive Systems: Processes, Practices, Methods, and Techniques_ _(DIS ’00)_. Association for Computing Machinery, New York, NY, USA, 396–405.  https://doi.org/10.1145/347642.347798
  * Flath et al. (2017) Christoph M. Flath, Sascha Friesike, Marco Wirth, and Frédéric Thiesse. 2017.  Copy, Transform, Combine: Exploring the Remix as a Form of Innovation.  _Journal of Information Technology_ 32, 4 (Dec. 2017), 306–325.  https://doi.org/10.1057/s41265-017-0043-9
  * Follmer et al. (2010) Sean Follmer, David Carr, Emily Lovell, and Hiroshi Ishii. 2010\.  CopyCAD: Remixing Physical Objects with Copy and Paste from the Real World. In _Adjunct Proceedings of the 23Nd Annual ACM Symposium on User Interface Software and Technology_ _(UIST ’10)_. ACM, New York, NY, USA, 381–382.  https://doi.org/10.1145/1866218.1866230
  * Follmer and Ishii (2012) Sean Follmer and Hiroshi Ishii. 2012.  KidCAD: Digitally Remixing Toys through Tangible Tools. In _Proceedings of the 2012 SIGCHI Conference on Human Factors in Computing Systems_. ACM, New York, NY, 2401–2410.  https://doi.org/10.1145/2207676.2208403
  * Fraser et al. (2020) C. Ailie Fraser, Julia M. Markel, N. James Basa, Mira Dontcheva, and Scott Klemmer. 2020.  ReMap: Lowering the Barrier to Help-Seeking with Multimodal Search. In _Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology_. ACM, Virtual Event USA, 979–986.  https://doi.org/10.1145/3379337.3415592
  * Garnier and Capdevila (2021) Constance Garnier and Ignasi Capdevila. 2021.  Making, Hacking, Coding: Fablabs as Intermediary Platforms for Modes of Social Manufacturing:.  _Journal of Innovation Economics & Management_ Prépublication, 0 (Dec. 2021), I–XXII.  https://doi.org/10.3917/jie.pr1.0128
  * Gershenfeld (2005) Neil Gershenfeld. 2005\.  _Fab: The Coming Revolution on Your Desktop–from Personal Computers to Personal Fabrication_.  Basic Books (AZ), New York, NY, USA.
  * Gershenfeld et al. (2017) Neil Gershenfeld, Alan Gershenfeld, and Joel Cutcher-Gershenfeld. 2017\.  _Designing Reality: How to Survive and Thrive in the Third Digital Revolution_.  Basic Books, Inc., USA.
  * Giunchi et al. (2021) Daniele Giunchi, Alejandro Sztrajman, Stuart James, and Anthony Steed. 2021.  Mixing Modalities of 3D Sketching and Speech for Interactive Model Retrieval in Virtual Reality. In _ACM International Conference on Interactive Media Experiences_. ACM, Virtual Event USA, 144–155.  https://doi.org/10.1145/3452918.3458806
  * Gruenefeld et al. (2022) Uwe Gruenefeld, Jonas Auda, Florian Mathis, Stefan Schneegass, Mohamed Khamis, Jan Gugenheimer, and Sven Mayer. 2022.  VRception: Rapid Prototyping of Cross-Reality Systems in Virtual Reality. In _Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems_ _(CHI ’22)_. Association for Computing Machinery, New York, NY, USA, 1–15.  https://doi.org/10.1145/3491102.3501821
  * Guo and Prabhakaran (2022) Hung-Jui Guo and Balakrishnan Prabhakaran. 2022.  HoloLens 2 Technical Evaluation as Mixed Reality Guide.  arXiv:2207.09554 [cs] http://arxiv.org/abs/2207.09554
  * Han et al. (2023) Violet Yinuo Han, Hyunsung Cho, Kiyosu Maeda, Alexandra Ion, and David Lindlbauer. 2023.  BlendMR: A Computational Method to Create Ambient Mixed Reality Interfaces.  _Proceedings of the ACM on Human-Computer Interaction_ 7, ISS (Nov. 2023), 436:217–436:241.  https://doi.org/10.1145/3626472
  * Hart and Staveland (1988) Sandra G. Hart and Lowell E. Staveland. 1988.  Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research.  In _Advances in Psychology_. Vol. 52. Elsevier, 139–183.
  * Hill and Monroy-Hernández (2013) Benjamin Mako Hill and Andrés Monroy-Hernández. 2013\.  The Cost of Collaboration for Code and Art: Evidence from a Remixing Community. In _Proceedings of the 2013 Conference on Computer Supported Cooperative Work_ _(CSCW ’13)_. Association for Computing Machinery, New York, NY, USA, 1035–1046.  https://doi.org/10.1145/2441776.2441893
  * Hofmann et al. (2018) Megan Hofmann, Gabriella Hann, Scott E. Hudson, and Jennifer Mankoff. 2018.  Greater Than the Sum of Its PARTs: Expressing and Reusing Design Intent in 3D Models. In _Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems_ _(CHI ’18)_. ACM, New York, NY, USA, 301:1–301:12.  https://doi.org/10.1145/3173574.3173875
  * Holz and Wilson (2011) Christian Holz and Andrew Wilson. 2011.  Data Miming: Inferring Spatial Object Descriptions from Human Gesture. In _Proceedings of the SIGCHI Conference on Human Factors in Computing Systems_ _(CHI ’11)_. ACM, New York, NY, USA, 811–820.  https://doi.org/10.1145/1978942.1979060
  * Hudson et al. (2016) Nathaniel Hudson, Celena Alcock, and Parmit K. Chilana. 2016\.  Understanding Newcomers to 3D Printing: Motivations, Workflows, and Barriers of Casual Makers. In _Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems_ _(CHI ’16)_. ACM, New York, NY, USA, 384–396.  https://doi.org/10.1145/2858036.2858266
  * Huo et al. (2017) Ke Huo, Vinayak, and Karthik Ramani. 2017.  Window-Shaping: 3D Design Ideation by Creating on, Borrowing from, and Looking at the Physical World. In _Proceedings of the Eleventh International Conference on Tangible, Embedded, and Embodied Interaction_ _(TEI ’17)_. ACM, New York, NY, USA, 37–45.  https://doi.org/10.1145/3024969.3024995
  * Hutton Pospick and Suma Rosenberg (2023) Courtney Hutton Pospick and Evan Suma Rosenberg. 2023.  Creating and Manipulating 3D Paths With Mixed Reality Spatial Interfaces.  _Frontiers in Virtual Reality_ 4 (2023), 1192757.  https://www.frontiersin.org/articles/10.3389/frvir.2023.1192757/full
  * Igarashi et al. (1999) Takeo Igarashi, Satoshi Matsuoka, and Hidehiko Tanaka. 1999\.  Teddy: A Sketching Interface for 3D Freeform Design. In _Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques_ _(SIGGRAPH ’99)_. ACM Press/Addison-Wesley Publishing Co., USA, 409–416.  https://doi.org/10.1145/311535.311602
  * Ipsita et al. (2021) Ananya Ipsita, Hao Li, Runlin Duan, Yuanzhi Cao, Subramanian Chidambaram, Min Liu, and Karthik Ramani. 2021.  VRFromX: From Scanned Reality to Interactive Virtual Experience with Human-in-the-Loop. In _Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems_ _(CHI EA ’21)_. Association for Computing Machinery, New York, NY, USA, 1–7.  https://doi.org/10.1145/3411763.3451747
  * Jahn et al. (2018) Gwyllim Jahn, Cameron Newnham, and Matthew Beanland. 2018\.  Making in Mixed Reality. Holographic Design, Fabrication, Assembly and Analysis of Woven Steel Structures. In _ACADIA 2018: Re/Calibration: On Imprecision and Infidelity_. Mexico City, Mexico, 88–97.  https://doi.org/10.52842/conf.acadia.2018.088
  * Jennings et al. (2022) Nicholas Jennings, Ananya Nandy, Xinyi Zhu, Yuting Wang, Fanping Sui, James Smith, and Bjoern Hartmann. 2022.  GeneratiVR: Spatial Interactions in Virtual Reality to Explore Generative Design Spaces. In _Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems_ _(CHI EA ’22)_. Association for Computing Machinery, New York, NY, USA, 1–6.  https://doi.org/10.1145/3491101.3519616
  * Jocher and Loch (2010) Thomas Jocher and Sigrid Loch. 2010.  _Raumpilot_.  Kraemer Verlag.
  * Kaimoto et al. (2022) Hiroki Kaimoto, Kyzyl Monteiro, Mehrad Faridan, Jiatong Li, Samin Farajian, Yasuaki Kakehi, Ken Nakagaki, and Ryo Suzuki. 2022\.  Sketched Reality: Sketching Bi-Directional Interactions Between Virtual and Physical Worlds with AR and Actuated Tangible UI. In _Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology_ _(UIST ’22)_. Association for Computing Machinery, New York, NY, USA, 1–12.  https://doi.org/10.1145/3526113.3545626
  * Karlen et al. (2017) Mark Karlen, Christina Spangler, and James R. Benya. 2017\.  _Lighting Design Basics_.  John Wiley & Sons.
  * Kim et al. (2005) Hyosun Kim, Georgia Albuquerque, Sven Havemann, and Dieter W. Fellner. 2005.  Tangible 3D: Hand Gesture Interaction for Immersive 3D Modeling. In _Proceedings of the 11th Eurographics Conference on Virtual Environments_ _(EGVE’05)_. Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, 191–199.  https://doi.org/10.2312/EGVE/IPT_EGVE2005/191-199
  * Kim et al. (2017) Jeeeun Kim, Anhong Guo, Tom Yeh, Scott E. Hudson, and Jennifer Mankoff. 2017.  Understanding Uncertainty in Measurement and Accommodating Its Impact in 3D Modeling and Printing. In _Proceedings of the 2017 Conference on Designing Interactive Systems_ _(DIS ’17)_. ACM, New York, NY, USA, 1067–1078.  https://doi.org/10.1145/3064663.3064690
  * Kim et al. (2021) Woojoo Kim, Jaeho Sung, Daniel Saakes, Chunxi Huang, and Shuping Xiong. 2021.  Ergonomic Postural Assessment Using a New Open-Source Human Pose Estimation Technology (OpenPose).  _International Journal of Industrial Ergonomics_ 84 (2021), 103164\.
  * Koch et al. (2020) Janin Koch, Nicolas Taffin, Andrés Lucero, and Wendy E. Mackay. 2020.  SemanticCollage: Enriching Digital Mood Board Design with Semantic Labels. In _DIS ’20 \- Designing Interactive Systems Conference 2020_. ACM, 407.  https://doi.org/10.1145/3357236.3395494
  * Kopp (2005) Stefan Kopp. 2005\.  The Spatial Specificity of Iconic Gestures. In _Proceedings of KogWis05. The German Cognitive Science Conference_. 112–117.
  * Kovacs (2021) Robert Kovacs. 2021\.  Human-Scale Personal Fabrication.  In _The Adjunct Publication of the 34th Annual ACM Symposium on User Interface Software and Technology_. Association for Computing Machinery, New York, NY, USA, 162–165.  https://doi.org/10.1145/3474349.3477588
  * Lafreniere and Grossman (2018) Ben Lafreniere and Tovi Grossman. 2018.  Blocks-to-CAD: A Cross-Application Bridge from Minecraft to 3D Modeling. In _Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology_ _(UIST ’18)_. Association for Computing Machinery, New York, NY, USA, 637–648.  https://doi.org/10.1145/3242587.3242602
  * Lao et al. (2021) Cheryl Lao, Haijun Xia, Daniel Wigdor, and Fanny Chevalier. 2021\.  Attribute Spaces: Supporting Design Space Exploration in Virtual Reality. In _Symposium on Spatial User Interaction_ _(SUI ’21)_. Association for Computing Machinery, New York, NY, USA, 1–11.  https://doi.org/10.1145/3485279.3485290
  * Ledo et al. (2018) David Ledo, Steven Houben, Jo Vermeulen, Nicolai Marquardt, Lora Oehlberg, and Saul Greenberg. 2018\.  Evaluation Strategies for HCI Toolkit Research. In _Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems_ _(CHI ’18)_. Association for Computing Machinery, New York, NY, USA, 1–17.  https://doi.org/10.1145/3173574.3173610
  * Lee et al. (2016) Bokyung Lee, Minjoo Cho, Joonhee Min, and Daniel Saakes. 2016\.  Posing and Acting As Input for Personalizing Furniture. In _Proceedings of the 9th Nordic Conference on Human-Computer Interaction_ _(NordiCHI ’16)_. ACM, New York, NY, USA, 44:1–44:10.  https://doi.org/10.1145/2971485.2971487
  * Lee et al. (2017) Bokyung Lee, Gyeol Han, Jundong Park, and Daniel Saakes. 2017\.  Consumer to Creator: How Households Buy Furniture to Inform Design and Fabrication Interfaces. In _Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems_ _(CHI ’17)_. ACM, New York, NY, USA, 484–496.  https://doi.org/10.1145/3025453.3025666
  * Lee et al. (2019) Bokyung Lee, Taeil Jin, Sung-Hee Lee, and Daniel Saakes. 2019\.  SmartManikin: Virtual Humans with Agency for Design Tools. In _Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems_. ACM, Glasgow Scotland Uk, 1–13.  https://doi.org/10.1145/3290605.3300814
  * Lee and Saakes (2022) Bokyung Lee and Daniel Saakes. 2022.  Understanding the Roles of Intelligent Product-Customization Systems Through Expert Interviews. In _[ ] With Design: Reinventing Design Modes_ , Gerhard Bruyns and Huaxin Wei (Eds.). Springer Nature, Singapore, 1153–1169.  https://doi.org/10.1007/978-981-19-4472-7_76
  * Lee et al. (2018) Bokyung Lee, Joongi Shin, Hyoshin Bae, and Daniel Saakes. 2018\.  Interactive and Situated Guidelines to Help Users Design a Personal Desk That Fits Their Bodies. In _Proceedings of the 2018 Designing Interactive Systems Conference_ _(DIS ’18)_. ACM, New York, NY, USA, 637–650.  https://doi.org/10.1145/3196709.3196725
  * Leigh and Maes (2015) Sang-won Leigh and Pattie Maes. 2015.  AfterMath: Visualizing Consequences of Actions through Augmented Reality. In _Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems_ _(CHI EA ’15)_. Association for Computing Machinery, New York, NY, USA, 941–946.  https://doi.org/10.1145/2702613.2732695
  * Liang et al. (2022) Chen Liang, Anhong Guo, and Jeeeun Kim. 2022.  CustomizAR: Facilitating Interactive Exploration and Measurement of Adaptive 3D Designs. In _Designing Interactive Systems Conference_ _(DIS ’22)_. Association for Computing Machinery, New York, NY, USA, 898–912.  https://doi.org/10.1145/3532106.3533561
  * Lili et al. (2010) Wang Lili, Shang Tao, Yang Zheng, and Li Yixiong. 2010\.  The Parametric Design of Office Chairs Based on Ergonomics. In _2010 International Conference on Computing, Control and Industrial Engineering_ , Vol. 1. IEEE, 355–360.
  * Lindlbauer and Wilson (2018) David Lindlbauer and Andy D. Wilson. 2018.  Remixed Reality: Manipulating Space and Time in Augmented Reality. In _Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems \- CHI ’18_. ACM Press, Montreal QC, Canada, 1–13.  https://doi.org/10.1145/3173574.3173703
  * Machuca et al. (2023) Mayra Donaji Barrera Machuca, Johann Habakuk Israel, and Daniel F. Keefe. 2023.  Toward More Comprehensive Evaluations of 3D Immersive Sketching, Drawing, and Painting.  _IEEE Transactions on Visualization and Computer Graphics_ (2023), 1–18.  https://doi.org/10.1109/TVCG.2023.3276291
  * Mahapatra et al. (2019) Chandan Mahapatra, Jonas Kjeldmand Jensen, Michael McQuaid, and Daniel Ashbrook. 2019.  Barriers to End-User Designers of Augmented Fabrication. In _Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems_ _(CHI ’19)_. ACM, New York, NY, USA, 383:1–383:15.  https://doi.org/10.1145/3290605.3300613
  * Matejka et al. (2018) Justin Matejka, Michael Glueck, Erin Bradner, Ali Hashemi, Tovi Grossman, and George Fitzmaurice. 2018\.  Dream Lens: Exploration and Visualization of Large-Scale Generative Design Datasets. In _Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems_ _(CHI ’18)_. Association for Computing Machinery, New York, NY, USA, 1–12.  https://doi.org/10.1145/3173574.3173943
  * Mayra Donaji Barrera Machuca et al. (2023) Mayra Donaji Barrera Machuca, Rahul Arora, Philipp Wacker, Daniel Keefe, and Johann Habakuk Israel. 2023.  Interaction Devices and Techniques for 3D Sketching.  In _Interactive Sketch-based Interfaces and Modelling for Design_ (1st edition ed.). River Publishers, New York.  https://doi.org/10.1201/9781003360650
  * McCrae et al. (2014) James McCrae, Nobuyuki Umetani, and Karan Singh. 2014\.  FlatFitFab: Interactive Modeling with Planar Sections. In _Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology_ _(UIST ’14)_. ACM, New York, NY, USA, 13–22.  https://doi.org/10.1145/2642918.2647388
  * McElroy and Huang (2023) Leo McElroy and Lingdong Huang. 2023.  PotScript: A Visual Grammar for Sculpting with Functions. In _Proceedings of the 8th ACM Symposium on Computational Fabrication_ _(SCF ’23)_. Association for Computing Machinery, New York, NY, USA, 1–9.  https://doi.org/10.1145/3623263.3623357
  * Michel and Boubekeur (2021) Élie Michel and Tamy Boubekeur. 2021.  DAG Amendment for Inverse Control of Parametric Shapes.  _ACM Transactions on Graphics_ 40, 4 (July 2021), 173:1–173:14.  https://doi.org/10.1145/3450626.3459823
  * Mitterberger et al. (2022) Daniela Mitterberger, Selen Ercan Jenny, Lauren Vasey, Ena Lloret-Fritschi, Petrus Aejmelaeus-Lindström, Fabio Gramazio, and Matthias Kohler. 2022.  Interactive Robotic Plastering: Augmented Interactive Design and Fabrication for On-site Robotic Plastering. In _Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems_ _(CHI ’22)_. Association for Computing Machinery, New York, NY, USA, 1–18.  https://doi.org/10.1145/3491102.3501842
  * Mota (2011) Catarina Mota. 2011\.  The Rise of Personal Fabrication. In _Proceedings of the 8th ACM Conference on Creativity and Cognition_ _(C &C ’11)_. Association for Computing Machinery, Atlanta, Georgia, USA, 279–288.  https://doi.org/10.1145/2069618.2069665
  * Müller et al. (2021) Leon Müller, Ken Pfeuffer, Jan Gugenheimer, Bastian Pfleging, Sarah Prange, and Florian Alt. 2021.  SpatialProto: Exploring Real-World Motion Captures for Rapid Prototyping of Interactive Mixed Reality. In _Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems_ _(CHI ’21)_. Association for Computing Machinery, New York, NY, USA, 1–13.  https://doi.org/10.1145/3411764.3445560
  * Nachtigall et al. (2019) Troy Nachtigall, Oscar Tomico, and Ron Wakkary. 2019\.  ONEDAY Shoes: A Maker Toolkit to Understand the Role of Co-Manufacturing in Personalization. In _Proceedings of the Thirteenth International Conference on Tangible, Embedded, and Embodied Interaction_ _(TEI ’19)_. ACM, New York, NY, USA, 105–115.  https://doi.org/10.1145/3294109.3295637
  * Navas (2014) Eduardo Navas. 2014\.  _Remix Theory: The Aesthetics of Sampling_.  Birkhäuser.
  * Nebeling (2022) Michael Nebeling. 2022\.  XR Tools and Where They Are Taking Us: Characterizing the Evolving Research on Augmented, Virtual, and Mixed Reality Prototyping and Development Tools.  _XRDS: Crossroads, The ACM Magazine for Students_ 29, 1 (Oct. 2022), 32–38.  https://doi.org/10.1145/3558192
  * Nebeling et al. (2018) Michael Nebeling, Janet Nebeling, Ao Yu, and Rob Rumble. 2018\.  ProtoAR: Rapid Physical-Digital Prototyping of Mobile Augmented Reality Applications. In _Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems_ _(CHI ’18)_. Association for Computing Machinery, New York, NY, USA, 1–12.  https://doi.org/10.1145/3173574.3173927
  * Nebeling and Speicher (2018) Michael Nebeling and Maximilian Speicher. 2018.  The Trouble with Augmented Reality/Virtual Reality Authoring Tools. In _2018 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)_. 333–337.  https://doi.org/10.1109/ISMAR-Adjunct.2018.00098
  * Nuernberger et al. (2016) Benjamin Nuernberger, Eyal Ofek, Hrvoje Benko, and Andrew D. Wilson. 2016.  SnapToReality: Aligning Augmented Reality to the Real World. In _Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems_ _(CHI ’16)_. ACM, New York, NY, USA, 1233–1244.  https://doi.org/10.1145/2858036.2858250
  * Oehlberg et al. (2015) Lora Oehlberg, Wesley Willett, and Wendy E. Mackay. 2015\.  Patterns of Physical Design Remixing in Online Maker Communities. In _Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems_ _(CHI ’15)_. ACM, New York, NY, USA, 639–648.  https://doi.org/10.1145/2702123.2702175
  * Openshaw and Taylor (2006) Scott Openshaw and Erin Taylor. 2006.  Ergonomics and Design A Reference Guide: Allsteel Inc.  _Muscatine, Iowa_ (2006), 52761–5257.
  * Ozdemir et al. (22ed) Mehmet Ozdemir, Jouke Verlinden, and Gaetano Cascini. 2022/ed.  Design Methodology for Mass Personalisation Enabled by Digital Manufacturing.  _Design Science_ 8 (2022/ed), e7.  https://doi.org/10.1017/dsj.2022.3
  * Pearce (2015) Joshua M. Pearce. 2015\.  Applications of Open Source 3-D Printing on Small Farms.  _Organic Farming_ 1, 1 (2015).
  * Peek and Moyer (2017) Nadya Peek and Ilan Moyer. 2017.  Popfab: A Case for Portable Digital Fabrication. In _Proceedings of the Eleventh International Conference on Tangible, Embedded, and Embodied Interaction_ _(TEI ’17)_. ACM, New York, NY, USA, 325–329.  https://doi.org/10.1145/3024969.3025009
  * Peng et al. (2018) Huaishu Peng, Jimmy Briggs, Cheng-Yao Wang, Kevin Guo, Joseph Kider, Stefanie Mueller, Patrick Baudisch, and François Guimbretière. 2018.  RoMA: Interactive Fabrication with Augmented Reality and a Robotic 3D Printer. In _Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems_ _(CHI ’18)_. ACM, New York, NY, USA, 579:1–579:12.  https://doi.org/10.1145/3173574.3174153
  * Quitmeyer and Perner-Wilson (2015) Andrew Quitmeyer and Hannah Perner-Wilson. 2015.  Wearable Studio Practice: Design Considerations for Digital Crafting in Harsh Environments. In _Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers_ _(UbiComp/ISWC’15 Adjunct)_. ACM, New York, NY, USA, 1285–1293.  https://doi.org/10.1145/2800835.2807926
  * Ramakers et al. (2023) Raf Ramakers, Danny Leen, Jeeeun Kim, Kris Luyten, Steven Houben, and Tom Veuskens. 2023\.  Measurement Patterns: User-Oriented Strategies for Dealing with Measurements and Dimensions in Making Processes. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_ _(CHI ’23)_. Association for Computing Machinery, New York, NY, USA, 1–17.  https://doi.org/10.1145/3544548.3581157
  * Reipschläger and Dachselt (2019) Patrick Reipschläger and Raimund Dachselt. 2019.  DesignAR: Immersive 3D-Modeling Combining Augmented Reality with Interactive Displays. In _Proceedings of the 2019 ACM International Conference on Interactive Surfaces and Spaces_ _(ISS ’19)_. Association for Computing Machinery, Daejeon, Republic of Korea, 29–41.  https://doi.org/10.1145/3343055.3359718
  * Ren et al. (2023) Haocheng Ren, Hangming Fan, Rui Wang, Yuchi Huo, Rui Tang, Lei Wang, and Hujun Bao. 2023.  Data-Driven Digital Lighting Design for Residential Indoor Spaces.  _ACM Transactions on Graphics_ 42, 3 (March 2023), 28:1–28:18.  https://doi.org/10.1145/3582001
  * Roumen et al. (2016) Thijs Roumen, Bastian Kruck, Tobias Dürschmid, Tobias Nack, and Patrick Baudisch. 2016\.  Mobile Fabrication. In _Proceedings of the 29th Annual Symposium on User Interface Software and Technology_ _(UIST ’16)_. ACM, New York, NY, USA, 3–14.  https://doi.org/10.1145/2984511.2984586
  * Saakes et al. (2015) Daniel Saakes, Hui-Shyong Yeo, Seung-Tak Noh, Gyeol Han, and Woontack Woo. 2015.  Mirror Mirror: An on-Body Clothing Design System. In _SIGGRAPH 2015: Studio_ _(SIGGRAPH ’15)_. Association for Computing Machinery, New York, NY, USA, 1\.  https://doi.org/10.1145/2785585.2792691
  * Saul et al. (2011) Greg Saul, Manfred Lau, Jun Mitani, and Takeo Igarashi. 2011\.  SketchChair: An All-in-one Chair Design System for End Users. In _Proceedings of the Fifth International Conference on Tangible, Embedded, and Embodied Interaction_ _(TEI ’11)_. ACM, New York, NY, USA, 73–80.  https://doi.org/10.1145/1935701.1935717
  * Shugrina et al. (2015) Maria Shugrina, Ariel Shamir, and Wojciech Matusik. 2015\.  Fab Forms: Customizable Objects for Fabrication with Validity and Geometry Caching.  _ACM Transactions on Graphics_ 34, 4 (July 2015), 100:1–100:12.  https://doi.org/10.1145/2766994
  * Soares et al. (2021) Inês Soares, Ricardo B. Sousa, Marcelo Petry, and António Paulo Moreira. 2021\.  Accuracy and Repeatability Tests on HoloLens 2 and HTC Vive.  _Multimodal Technologies and Interaction_ 5, 8 (2021), 47\.  https://www.mdpi.com/2414-4088/5/8/47