# OperARtistry: An AR-based Interactive Application to Assist the Learning of
Chinese Traditional Opera (Xiqu) Makeup

Zeyu Xiong  0000-0002-3652-1890 Computational Media and Arts ThrustThe Hong
Kong University of Science and Technology (Guangzhou)GuangzhouChina
<EMAIL_ADDRESS>,  Shihan Fu  0009-0005-3019-6600
Computational Media and Arts ThrustThe Hong Kong University of Science and
Technology (Guangzhou)GuangzhouChina<EMAIL_ADDRESS>and
Mingming Fan  0000-0002-0356-4712 Computational Media and Arts ThrustThe Hong
Kong University of Science and Technology (Guangzhou)GuangzhouChina The Hong
Kong University of Science and TechnologyHong Kong SAR, China
<EMAIL_ADDRESS>
(2023)

###### Abstract.

Chinese Traditional Opera (Xiqu) is an important type of intangible cultural
heritage and one key characteristic of Xiqu is its visual effects on face
achieved via makeup. However, Xiqu makeup process, especially the eye-area
makeup process, is complex and time-consuming, which poses a learning
challenge for potential younger inheritors. We introduce OperARtistry, an
interactive application based on Augmented Reality (AR) that offers in-situ
Xiqu makeup guidance for beginners. Our application provides a step-by-step
guide for Xiqu eye-area makeup, incorporating AR effects at each stage.
Furthermore, we conducted an initial user study (n=6) to compare our approach
with existing video-based tutorials to assess the effectiveness and usefulness
of our approach. Our findings show that OperARtisty helped participants
achieve high-quality eye-area makeup effects with less learning time.

Assistive Technology, User-Centered Design, Intangible Cultural Heritage,
Augmented Reality, Computer Aided Instruction

††journalyear: 2023††copyright: acmlicensed††conference: Chinese CHI 2023;
November 13–16, 2023; Denpasar, Bali, Indonesia††booktitle: Chinese CHI 2023
(CHCHI 2023), November 13–16, 2023, Denpasar, Bali, Indonesia††price:
15.00††doi: 10.1145/3629606.3629622††isbn: 979-8-4007-1645-4/23/11††ccs:
Human-centered computing Empirical studies in HCI††ccs: Human-centered
computing Assistive Technology

##  1\. Introduction

Chinese traditional opera, also known as Xiqu, is listed on the Intangible
Cultural Heritage (Beijing Opera, Cantonese Opera, Kunqu, Tibetan Opera, and
Shadow Puppets) of UNESCO (UNESCO, [n. d.]). Prior work has investigated ways
to preserve (Zhang et al., 2019) and promote (Chen, 2014; Lu et al., 2019)
Xiqu. It often takes years of training to hone their skills and effectively
portray the character traits of the roles they play(An, 2006). According to
Pang (2005) and Wichmann (1990), characters are easier for the audience to
recognize when their appearance and clothing are exaggerated. This makes Xiqu
art, even if it is a form of music, quite demanding in terms of visual
effects. Li and Li (2021) noted that in order to ensure the quality of the
visual effects, Xiqu Cosmetics frequently employs grease paint, which is quite
different from everyday cosmetics and frequently contains ingredients that are
detrimental to the face (Wang et al., 2023, 2020). At the same time, the
process of applying face makeup is time-consuming and complex, as explained by
Liu (1997). The performers use colorful paintings to paint a variety of
symbols and line art on their faces, which emphasize the characteristics,
positions, ages, and provenances of the characters. Xiqu face-painters must
take into account the individual shape and features of their face upon which
they are working (Wang et al., 2013). Moreover, the choice of facial colors
and the direction of the lines is complex. Furthermore, Xiqu makeup kit, which
includes brushes, water-based paints, powder, and oil-based paints (Wang,
1984), differs from traditional makeup kits. Thus, for beginners who are
unskilled in the Xiqu make up techniques, they often need to invest
significant efforts to learn them. One popular way to learn make up is by
watching online make up instructional video tutorials (Riboni, 2017). Indeed,
using online video tutorials is also a main way to learn Xiqu makeup on social
media. However, these online tutorials are often non-interactive and cannot
take into account the unique facial features of each individual learner. Thus,
beginners have to make guesses and trial-and-error when following video
tutorials, which are ineffective and can lead to many errors.

Augmented Reality (AR) allows digital content to be overlaid in the real
world, creating an interactive and immersive experience. We chose to
incorporate AR into our work to provide Xiqu makeup learners with a more
engaging and intuitive way to learn complicated makeup techniques. With AR,
learners can see the virtual effect of makeup applied to their own faces in
real-time, allowing them to observe potential issues promptly and make
adjustments. Moreover, we leveraged AR to provide step-by-step guidance and
feedback, making the learning process more interactive and personalized. In
sum, by leveraging AR technology in our design, we sought to make Xiqu makeup
learning process more accessible and enjoyable. Specifically, we sought to
answer the following research questions (RQs):

RQ1: What challenges are associated with learning Xiqu makeup?

RQ2: How to design an AR-based interactive learning approach to help beginners
to learn Xiqu makeup that can address the challenges that they encounter?

RQ3: How do AR-based interactive learning approach help beginners learn to
make Xiqu makeup compared to video tutorials?

We first conducted semi-structured interviews with 12 Xiqu artists with
varieties of practical experience in Xiqu performance and found the challenges
of learning Xiqu makeup, and needs for detailed and personalized makeup
instructions. Based on the findings, we derived four design considerations and
designed an interactive AR-based app, OperARtistry, which provides Xiqu makeup
beginners with real-time polylines and step-by-step instruction features to
better learn the makeup process. We picked the eye area, which is one of the
important regions of the face, for this initial evaluation. We recruited 6
participants, who were interested in Xiqu makeup and had different levels of
daily makeup skills, to participate in a comparative study, where they used
both our AR-based approach (i.e., OperARtistry) and a traditional video
tutorial approach. Results show that OperARtistry helped participants achieve
higher quality makeup results in terms of color, shape, completion time,
effect, and similarity, compared with baseline. Overall, this work lays the
foundation for further exploration and advancements in Xiqu makeup guidance.
Moreover, the insights gained from our work have the potential for broader
generalizability beyond the context of traditional Xiqu makeup. They can be
extended to various artistic fields that encompass complex visual effects,
opening up new possibilities for enhancing makeup techniques and aesthetics.

##  2\. Background and Related Work

Our work has been informed by the prior work investigating Xiqu Face Painting
Background and Assistive Technologies for Doing Makeup.

###  2.1. Xiqu Face Painting Background

In Xiqu facial decoration, there are two main techniques used: make up
technique and face painting technique (Delza, 1971). The makeup technique is
used to enhance the natural appearance of the face by intensifying desired
pink and white skin tones or accentuating features with exaggerated colors.
However, it is important that the face still appears natural, and only minimal
touch-ups are made to the eyes and eyebrows to make them appear more
realistic. On the other hand, the face painting technique is used to create a
painted picture of the face, which can sometimes result in the effacement of
actual facial features. For example, eyes may be drawn on the forehead or
cheeks, and noses and mouths may be distorted. The use of these two techniques
depends on the character traits that the Xiqu actors need to convey.

The performers use colorful paintings to paint a variety of symbols and line
art on their faces, which emphasize the characteristics, positions, ages, and
provenances of the characters. In Xiqu, the symbolism of the painted face is
typically classified based on color and configuration (Liang, 1980), with six
primary and six secondary colors, in addition to gold and silver, generally
acknowledged in facial painting. The painted face configuration is complex,
comprising numerous, at least over 500, painted face characters, excluding
regional variations, with major facial delineations consisting of eyebrows,
eye sockets, nose, mouth, and forehead. The combination of color and shape in
each of these areas not only suggests the image of a historical personage but
also captures the personality of a Xiqu character.

Figure 1. The make up Steps of the Four Main Roles in Chinese Opera (Wang et
al., 2013), Left: Sheng, Top: Dan, Right: Chou, Buttom: Ching

The Xiqu character’s make up process involves multiple steps that use
different dyes and brushes to apply the colors, as shown in the make up Steps
of the Four Main Roles in Chinese Opera (see Figure 1). For the Jing role, Kao
Teng, the makeup processes are: 1) A base of white powder is applied, and
black powder is applied around the eyes to prevent oil-based makeup from
seeping into them during a performance; additional black powder is applied
under the nose to prevent the beard from slipping off the actor’s face. 2) A
brush dipped in white paint is used to draw the approximate positions of the
eyebrows, eyes, nose, and facial patterns. Item: Black oil-based paint is
applied to unpainted areas. 3) Another layer of white paint is added to set
the facial pattern in place. 4) The pink powder is dusted onto the cheeks over
the white paint. 5) A single line of oil-based red paint is applied to the
forehead 111http://jinju.koo.org.tw.

Among the major facial delineations in Xiqu makeup, the eye-area, including
eye sockets and eyebrows, is particularly important in conveying the emotions
and personalities of the characters. The shape and color of the eyebrows, for
instance, can suggest the age, gender, and temperament of the character, while
the eye sockets can convey the character’s mood and intentions. As such,
mastering eye-area makeup techniques is crucial for learners to accurately
portray Xiqu characters on stage.

Furthermore, the predominant approach to learning Xiqu makeup through social
media relies on video instructions. However, online makeup tutorials often
lack interactive features that consider individual differences in skin and
face format, resulting in variations in product application. This limitation
becomes evident as video tutorial presenters may not be well-versed in
addressing the unique needs of each user.

Due to the complexity of Xiqu makeup and the lack of interactive guidance in
online tutorials, many learners struggle to achieve the desired results,
especially in the eye area. Therefore, we are motivated to explore an
interactive approach to making Xiqu makeup learning process more accessible
for beginners, for example, by providing learners with personalized guidance
and real-time feedback.

###  2.2. Assistive Technologies for Doing Makeup

####  2.2.1. Makeup try-on & Alternatives

Many studies have been conducted in order to propose technical improvements
for assisting makeup or simulating makeup facial try-on effects. Iwabuchi et
al. (2009) presented Smart Makeup Mirror, a system that assists the makeup
process using a high-resolution camera above a screen display, with functions
including displacing face from different angles and providing different
lighting conditions. Treepong et al. (2018) presented an interactive face
makeup system to provide a virtual makeup experience, consisting of a face
painting application that projects makeup effects onto the face and a set of
touch-detectable makeup tools that enable users to feel the experience of real
makeup. The iMake is a computer-aided eye makeup design system developed by
Nishimura and Siio (2014), which creates eye makeup designs using images
chosen by the user, moreover, these designs can be previewed in an augmented
simulation system and can be transferred onto the user’s eyelids using printed
transfer sheets. Kao et al. (2016) proposed a dynamic color-changing make up
system that allows the wearer to seamlessly change their appearance.
Specifically, they have designed an interactive eye shadow tattoo prototype
consisting of thermochromic pigments that allow the user to achieve a color-
changing banding effect. However, due to the uniqueness of the Xiqu makeup,
there is no evidence yet that these alternatives have been applied to the
traditional Xiqu makeup, and traditional oil paints are still widely used to
achieve desired visual effects in Xiqu.

####  2.2.2. Makeup aid for specific scenes & user group

In the HCI community, researchers have tried various ways of making the makeup
aid more accessible and interactive. For example, Truong et al. (2021a) has
created a system for automatically generating hierarchical makeup video
tutorials. The study of facial makeup for specific scenes, like theater drama,
has also been conducted. Cai and Yu (2010) presented a makeup design support
system for the Peking Opera, which combines the eye, nose, and mouth designs
to generate new designs. Users can view these designs on a 3D-rendered model.
Moreover, prior work has also investigated technologies for assisting people
with disabilities with make up. For example, Li et al. (2022) suggested the
practices and challenges of makeup for people with visual impairments, and
interactive feedback is mentioned as one of the key points to making the
makeup process more accessible. Additionally, researchers have investigated
makeup-wearing prediction. For example, Shi et al. (2022) has developed an
algorithm that provides visual aids for makeup product selection. To our
knowledge, how to design an interactive approach to help people better learn
Xiqu makeup with less effort remains to be an open research question.

####  2.2.3. Augmented reality system

Regarding Augmented Reality (AR) techniques, there are lots of work and
applications for facial try-on, for example: (1) AR makeup try-on (Borges and
Morimoto, 2019; Marelli et al., 2022; Evangelista et al., 2018), (2) AR makeup
creativity enhancement (Treepong et al., 2018), (3) AR face masks
(Premarathne, 2022; Liu et al., 2020; Al Hamzy et al., 2023; Javornik et al.,
2017), etc. Such techniques typically require fundamental facial detection
algorithms to extract facial feature points (Kumar et al., 2019). Moreover,
they are often designed to demonstrate makeup effects rather than in-depth
makeup tutorials. Furthermore, fewer, if any, AR applications are designed for
opera face painting. The existing virtual makeup tools have also been embedded
in the special effects of certain retouching apps (e.g. MeiTuXiuXiu
222https://pc.meitu.com/, Douyin 333https://www.douyin.com/, etc), however,
these applications are mainly for entertainment and do not reach the
requirements for Xiqu lovers as well as makeup learners. Therefore, even
though AR make up is widely used, there are few systems made to incorporate
the characteristics of Xiqu make up, and for Xiqu enthusiasts to participate
in AR-enabled theatre make up. Thus, in this work, we sought to address this
gap by designing and evaluating AR-based interactive app to assist beginners
to learn Xiqu makeup.

##  3\. FORMATIVE STUDY

To address RQ1, we conducted a formative study that involved semi-structured
interviews with 12 Xiqu artists. The primary objective of this study was to
gain initial insights into the experiences of learning Xiqu makeup, with a
specific focus on the challenges encountered during the makeup process. The
findings from this formative study highlight the user needs and preferences,
which served as valuable guidance for the design of a prototype aimed at
assisting beginners in learning the art of Xiqu makeup.

Table 1. Basic demographic information of the participants in semi-structured interviews Id | Gender | Age | Years of Xiqu Makeup Experience | Opera Type | Avg Face Paint Wearing Frequency
---|---|---|---|---|---
1 | M | 15-18 | 6-10 | Cantonese opera | 2-3 times / week
2 | F | 26-35 | 1-5 | Beijing Opera | 1-2 times / month
3 | F | 15-18 | >10 | Beijing Opera | 1-2 times / month
4 | M | 18-25 | 1-5 | Beijing Opera | 1-2 times / month
5 | F | 18-25 | 6-10 | Yue Opera | >3 times / week
6 | F | 18-25 | 1-5 | Kunqu | once a week
7 | F | 18-25 | 1-5 | Wuxi Opera | once a week
8 | F | >55 | >10 | Beijing Opera | 2-3 times / week
9 | F | 36-45 | >10 | Cantonese opera | once a week
10 | F | 15-18 | 6-10 | Cantonese opera | 1-2 times / month
11 | M | 36-45 | >10 | Kunqu | >3 times / week
12 | M | 26-35 | 6-10 | Henan Opera | 1-2 times / week

###  3.1. Semi-structured Interviews

We conducted online semi-structured interviews with 12 Xiqu artists (4 male, 8
female), with a wide variety of practical experience in Xiqu performance (See
Table 1). Each interview session lasted about one hour and every participant
was paid in accordance with local standards. The interview focused on how they
learned to apply Xiqu makeup, the steps to apply Xiqu makeup, specific tools,
and cosmetics. To analyze the interview data, we followed the thematic
analysis (Oktay, 2012) method. The open-coding procedure was carried out
independently by the two co-authors once they had familiarized themselves with
the data by reading the interview transcripts. Deductive and inductive coding
techniques were combined, and we set up themes of ”practices, challenges” for
the coding process. To create a unified codebook, the two coders regularly
discussed the codes and worked out their differences. All co-authors were
invited to additional meetings to reach agreements based on the initial coding
results. We summarize our tags related to the two themes in the research
questions at the end.

###  3.2. Findings

We present our findings from the formative study in this section and highlight
particular features of the Xiqu makeup.

####  3.2.1. Challenges faced when learning make up.

Even with in-person teaching from trained instructors, we found that novices
can find it challenging to completely master the art of Xiqu makeup, while
this kind of instruction has limited access for a wide range of learners. All
these findings highlight the need to design a makeup tutorial app for them.

##### Importance of makeup order.

All of our participants described their Xiqu makeup routines in a specific
order, in contrast to daily makeup routines, which have no predefined order.
This is due to the unique nature of Xiqu cosmetics, which require that the oil
painting be applied before the eyeshadow powder. If not, the makeup will come
off easily and the color effect will be unsatisfactory (P3: ” Applying the oil
painting first helps create a smooth base for the eyeshadow to adhere to,
also, following a step-by-step approach ensures that each component of the
Xiqu makeup is applied correctly and maximizes the lasting effects of the
makeup”). This leads to our first design consideration DC1: The Makeup
tutorial should be presented step by step.

##### Difficulty of eye makeup.

The majority of our participants (N=10) rated applying makeup to the eye area
as the most challenging step in the entire makeup process. This is due to a
number of factors, such as the sunken eye structure and the need for extreme
symmetry on the eye part (P8: ”Creating symmetrical eyeliner in the precise
area requires much practice and patience. Eye makeup, in particular, is the
most intricate part of the process due to its multi-layered nature and the
need for various shades of cosmetics. On the other hand, the rest of the face
simply requires applying oil-based paints without any specific difficulties”).
This derived DC2: The Makeup tutorial should offer more help for eye makeup.

####  3.2.2. Needs for detailed and personalized Makeup instructions

During our interviews, we discovered that the artistry of Xiqu make up extends
beyond the selection of appropriate cosmetics. It also involves the
consideration of each individual’s distinct facial features to make necessary
adjustments. This finding offers valuable insights and design considerations
for developing our system with detailed and personalized makeup instructions.

##### The variety and complexity of cosmetic tools.

Xiqu cosmetics, which use distinctive brushes and colors, are different from
common cosmetics. While the proper use of brushes can make the makeup process
more effective, the color choice of cosmetics is the key to present Xiqu
characters’ personalities (P11: ”I would adjust the color on my face to fit
the character). As a result, we should standardize the cosmetics accordingly -
DC3: The Makeup tutorial should specify cosmetic tools and color choices.

##### The adjustment of makeup for different face features.

Some of our participants (P3, P4, P8) mentioned that they customize their Xiqu
makeup based on their unique facial features, Specifically, they emphasized
that Xiqu makeup is not fixed and, moreover, may require adjustments to
enhance symmetrical or larger eyes (P3: ”I need to magnify my eyes with makeup
to make them more attractive”). Therefore, it is essential to adjust makeup
tutorials according to individual features - DC4: The application should pay
attention to each person’s individual facial features.

##  4\. System Design

Informed by the formative study findings and the derived DCs, we designed an
interactive AR-based app to assist Xiqu enthusiasts with their facial make up
efforts. Our app focuses on assisting with eye area make up (DC2). The app
provides instructions in a step-by-step format (DC1), accompanied by
corresponding auxiliary lines and coloring instructions, which are applied
progressively on the face. Such an approach affords a more intuitive and
precise make up guide. Also, The matching cosmetics are identified in the
application and are clearly numbered (DC3). The AR technology offers
customized makeup tutorials by locating certain facial points, taking into
account each user’s individual facial feature (DC4). The procedure is
specified in Figure 2.

Figure 2. System Design: AR Opera Paintings Tutorial. The Left shows a makeup
reference (intended completion), the Right shows interactive tutorial sample
steps, and each step includes text-based instructions (including makeup tool &
powder selection advice) and real-time auxiliary lines. Specifically: (A)
Step1, pet the foundation evenly onto the whole face with bare hands, (B)
Step2, smudge the blush onto the eye area with fingers, (C) Step3, use brush 1
and dye 1 to paint the eyeliner.

Beyond the basic design, we also consider the “flip” and “on / off”
functionalities. The “flip” function is used to flip the camera, which helps
the user check whether the makeup is left-right symmetrical or not, while the
“on / off” function is used to let users choose whether they want to turn on
AR effects in order to see the difference between the assist and themselves in
the real mirror. Figure 3444The blurring effect of the figure is added
manually for authorship anonymization shows the final application’s “tutorial
page”. The main camera module is in the center of the interface. Steps & tool
description are on the upper left, while on the upper right, there are 3
buttons for “flip”, “on / off”, and “on debug” (used to check the face mesh
alignment). Two buttons at the bottom are used to switch to the previous/next
step of the tutorial.

Figure 3. Final Prototype of OperARtistry: Tutorial Page Demonstration. The
real-time AR camera is positioned in the central. Top Left shows the step &
tools description. Top Right is the feature hub (flip the camera, “on / off”
the AR effects, and show debug mode). The bottom has 2 buttons to transfer
next or previous steps.

For the content of the tutorial, we mainly focus on “Eye Makeup” learning. We
have studied the steps of professional makeup artists and broken them down. We
finally designed the tutorial for 9 steps (shown in figure 4) with tools
descriptions in detail (shown in Figure 5): 1) Blush Rouge Level1 Use Brush
No.1 and Greaspaint No.1 to fill over the corresponding area. 2) Blush Rouge
Level2 Use Powder Puff to smudge the oil evenly over the corresponding area.
3) Blush Rouge Level3 Brush No.1 and Greaspaint No.2 to fill over the
corresponding area. 4) Blush Rouge Level4 Use Powder Puff to smudge the oil
evenly over the corresponding area. 5) Makeup Setting Apply Loose Powder
evenly over the entire face. 6) Eye Shadow Rouge Level1 Use Brush No.2 and
Toner No.1 to fill over the corresponding area. 7) Eye Shadow Rouge Level2 Use
Brush No.2 and Toner No.3 to fill over the corresponding area. 8) Upper
Eyeliner Use Eyeliner for outlining the upper area of the eye. 9) Lower
Eyeliner Use Eyeliner for outlining the lower area of the eye.

Figure 4. Nine Procedures of Eye Makeup Tutorial. Users need to finish 4
levels of blush rouge, makeup setting, 2 levels of eye shadow rouge, and upper
& lower eyeliner in a row. Figure 5. Eye Makeup Tutorial: Tools for Makeup. We
provide 3 types of toners, 2 types of brushes, 2 types of grease paints, 1
eyeliner, 1 loose powder, and 1 powder puff for users to complete the
tutorial.

##  5\. System Implementation

###  5.1. Overview

For the system implementation, we build a web application that makes sure all
the functionalities work as expected. We refer to some open-sourced materials
in the development, for example: (1) DeepAR SDK ((Terzopoulos et al.,
2021))555https://www.deepar.ai/, (2) Perfect Crop
666https://www.perfectcorp.com/business/technologies/makeup-ar, (3) Aryel
777https://aryel.io/solutions/virtual-try-on/, etc. We finally used React.js
888https://react.dev/ web application framework plus three.js
999https://threejs.org/ (a 3D library in JavaScript), on top of which we
introduced a facial feature point localization algorithm and an enhanced logic
step.

###  5.2. Facial Feature Points Detection

To achieve the effect of makeup steps floating on the face, we finish the
positioning of the makeup map with the face by recognizing the facial feature
points. There are lots of state-of-the-art facial feature point detection
methods that have been applied in practice (e.g. (Liu et al., 2023; Xiong et
al., 2022)), and in order for the system to implement the method in real-time
and without latency, we made adaptations in google’s facemesh model ((Ansari
et al., 2007; Sangal, 2022)) 101010https://developers.google.com/ml-
kit/vision/face-mesh-detection as well as the tensorflow.js (Smilkov et al.,
2019) framework with the help of webGL rendering technology. Based on the
results of facemesh, we can obtain 468 facial feature points simultaneously.

###  5.3. Facial Feature Points Alignment

After detecting 468 feature points on the face, We need to match the prepared
image to the corresponding feature points. According to Figure 6, we can two-
dimensionalize the feature points into an image, based on which we can draw
the desired pattern on the image. We then imported the pattern into the
system, and the process of changing from 2D to 3D was completed again. In the
end, we get the facial effect under the real-time camera.

Figure 6. Feature Points Alignment: from 2-Dimension to 3-Dimension. Left: a
standard face mesh with 468 facial feature points; Middle: add customized
colorful modules in 2-dimension; Right: map 2-dimension face mesh into
3-dimension, and show effects on real-time camera.

##  6\. User Study

###  6.1. Participants

We recruited 6 participants (2 males, 4 females, aged 22–28) who possess a
genuine interest in Xiqu and are enthusiastic about the Xiqu makeup process by
posting recruitment messages. To establish a comprehensive understanding of
our participants’ backgrounds and expertise in makeup, we administered an
online recruitment survey prior to the study. This survey allowed us to assess
participants’ familiarity with make up tools and their prior experience in
make up application. Subsequently, we classified participants into three
distinct proficiency levels (novices, competent, and proficient) based on
their responses in the recruitment survey. This categorization enabled us to
examine the influence of varying levels of makeup expertise on the outcomes of
our study. For a detailed overview of participants’ backgrounds and
proficiency levels, please refer to Table 2.

Table 2. Participants’ Background Information and Proficiency Levels

Participant ID | Gender | Age | Familiarity with make up1 | Proficiency Level
---|---|---|---|---
P1 | Female | 23 | High | Proficient
P2 | Male | 23 | Moderate | Novice
P3 | Female | 26 | Low | Competent
P4 | Female | 22 | Moderate | Competent
P5 | Male | 22 | Low | Novice
P6 | Female | 28 | High | Proficient

1 Proficiency levels were determined based on participants’ self-reported
familiarity with make up tools and their make up experience, as assessed in
the online recruitment survey.

###  6.2. Apparatus and Settings

We offer participants two kinds of Xiqu makeup tutorials: AR-based make up
application (OperARtistry) and video makeup tutorial. For the AR-based makeup
application, we utilized a computer equipped with a built-in camera as the
primary device. In contrast, the video makeup tutorial
111111http://xhslink.com/k7cxVp, duration: 19s - 27s was curated from
professional Xiqu makeup artists who have garnered a high recommendation index
on popular social media platforms. We meticulously reviewed and selected
videos that closely aligned with the makeup steps we had identified during our
field research. Specifically, we focused on identifying videos with
comprehensive instructions and demonstrations of the eye makeup process, as it
is a critical aspect of Xiqu makeup. The selected video tutorials served as
standardized references for participants and ensured consistency across the
study.

To maintain consistency in the study environment, participants utilized the
same computer to access the tutorial content. This approach ensures that
participants experience the tutorials in a controlled and uniform manner.

###  6.3. Procedure

The user study consisted of four distinct parts, as illustrated in Figure 7.
All study procedures were conducted in a dedicated, empty room with the
explicit consent of the participants. The experimenter interacted with the
participants in a face-to-face setting, ensuring direct engagement and real-
time observation of their makeup application process. Each part of the study
is described in detail below.

Part One. In this initial phase, participants were presented with a frontal
view of a standard Xiqu eye makeup, accompanied by an oblique side view, to
provide them with a comprehensive understanding of the desired makeup effect
and intricate details. Participants were given the opportunity to continuously
refer to this image throughout the makeup application process, allowing them
to maintain a clear visual reference.

Part Two. Once participants indicated their familiarity with the Xiqu eye
makeup effect, the second part of the study focused on introducing them to the
content of the video tutorial and the fundamental functionalities of the AR-
based tutorial. Participants were granted autonomy to explore and experience
the usage of both makeup tutorial tools, gaining hands-on familiarity with
their features and capabilities.

Part Three. After familiarizing themselves with the basic functionalities of
the tools, participants proceeded to apply makeup with the assistance of two
types of tutorials. The provided makeup tools were used to maintain
consistency among participants. In order to minimize any potential order
effects, the sequence of the tutorials was presented in different orders to
participants using a balanced Latin Square design, which effectively
distributed any bias.

Part Four. The objective of this section was to gather detailed insights into
participants’ experiences and perceptions of the AR-based makeup application
in comparison to the video tutorial. First, the user will complete a scale
from 1 to 5, from the final makeup’s perspective of color, shape, speed,
effect, and similarity. Next, a semi-structured interview was conducted with
each participant in the same room. Sample interview questions encompassed
various aspects, such as overall impressions of the AR makeup experience,
utilization of different AR makeup tutorial features, identification of useful
and less useful features, and an evaluation of the advantages and
disadvantages of AR makeup in relation to video makeup.

Figure 7. The User study process: (1) showing participants the intended eye
makeup effect, (2) introduction session of the makeup video and the AR
application, (3) experiencing makeup tutorial by AR application and baseline,
(4) a semi-structured interview after experiencing the tutorial.

##  7\. Results

In this section, we present results and evaluations of the system performance
and user experience regarding the AR makeup application in comparison to
traditional video makeup tutorials. By capturing participants’ viewpoints, we
gain insights into their overall experiences and evaluations of the AR-based
approach.

###  7.1. Performance Evaluation

As discussed in section 6.3, we collect rating data among several attributes
(rating scale 1-5) from the participants. Thus, we summarized these data as a
whole, and figure 8 shows the detailed average rating data with the error bar
(calculate by the standard deviation divided by the square root of the sample
capacity) from 6 participants.

Figure 8. Participants’ Avg Ratings on AR & Video Tutorials (scale: 1 - 5)

Besides, we also recorded the number of clicks & completion time that the
participants clicked to the video “play & pause” and AR “on & off” buttons.
The details are shown in table 9.

Figure 9. Avg & Std value of Completion Time & Number of Clicks

####  7.1.1. Finding 1: General Performance – Victory of AR Tutorial

Overall, all rating metrics (color, shape, speed, effect, and similarity)
comparison show the same result: The average values of all AR metrics are
higher than the results under the video tutorial. Although the error bar
participants seem more controversial on AR, this result indicates that
participants were more satisfied with all metrics of the AR aids than the
video tutorials, and there was a convergence of opinion among the
participants.

####  7.1.2. Finding 2: The Smoothness Experience of AR Tutorial

From the comparison of the indicators, we can see that AR and video tutorials
have the largest difference in the average score of the speed indicator.
However, in terms of the average completion time, participants spend more time
on AR than on the video tutorial. According to the results of the semi-
structured interview, we found that the reason is that the AR gives more
detailed instructions, and the users pay more attention to details in order to
achieve better results. Even though the completion time has increased, the
smoothness of the experience has improved. Besides, though the average number
of clicks in AR is slightly lower than in the video tutorial, AR provides a
smoother make up experience compared to video tutorials in this aspect,
subjective evaluation results suggest that the difference can be attributed to
the fact that video tutorials require users to watch the entire video without
directly interacting with the make up, unlike AR tutorials, where users can
work with the make up directly. This discrepancy potentially contributes to an
overall less smooth makeup experience with video tutorials.

###  7.2. User Experience Evaluation

####  7.2.1. Time Efficiency

We employed a systematic approach by dividing the eye makeup process into nine
distinct parts and assigning corresponding cosmetics in numerical order.
During the semi-structured interviews, participants were asked to evaluate the
makeup application time based on their experience regarding the overall
fluidity of the process. The majority of participants (5 out of 6) expressed
that this organizational structure significantly enhanced the fluidity of the
makeup process. Consequently, they expressed higher satisfaction with the time
efficiency when compared to video makeup instruction (P5: ”The clarity of
knowing which cosmetics to use at each step and where to apply them precisely
enhances the overall flow of the makeup process.”). Even participants with
prior makeup experience echoed this sentiment (P6: ”It prevents me from
feeling overwhelmed, unlike when doing my own makeup, where I often have to
contemplate for extended periods of time.”). However, a minority of
participants raised concerns about the reliance on the guidance provided by
the AR makeup tutorial due to the absence of visual representations of the
rendering effect for each step. This reliance, according to them, hindered the
smoothness of the makeup application process (P3: ”I may have become overly
dependent on AR. While I follow the instructions for each step, I don’t have a
clear understanding of how the final outcome of each step should appear,
making it difficult for me to make effective adjustments.”).

####  7.2.2. Click Actions: Pause and Slide

In addition to the ON/OFF and Flip functions, participants provided feedback
on the click actions associated with these features. Their comments varied,
highlighting different perspectives on the functions themselves and the ease
of performing the clicking actions.

Regarding the ON/OFF function, some participants found it beneficial,
particularly appreciating the screen’s ability to act as a mirror when the
makeup effect was turned off (P4: ”The ON/OFF feature initially worked well. I
could deactivate the makeup effect and easily assess my eye makeup.”).
However, the majority of participants adopted a neutral stance toward this
feature due to the pixelation caused by the built-in camera of the computer
they were using. They believed that the makeup displayed on the screen was not
as clear as when using a traditional mirror (P2: ”During the initial steps of
my makeup, the blurry image captured by the camera made it challenging to
perceive the colors on my face clearly.”).

Regarding the clicking action itself, Participant 1 expressed a desire for a
more convenient alternative to using the touchpad, suggesting the use of
shortcut keys on the keyboard instead. Nevertheless, participants acknowledged
that, compared to video makeup instruction, the AR-based tutorial required
significantly fewer clicks, eliminating the need for interactive actions such
as sliding the progress bar. This reduction in click frequency was viewed as a
time-saving advantage (P1: ”When using video instructions, I often have to
click to pause at specific moments and sometimes drag the progress bar to
replay a section, which can be time-consuming.”).

In contrast, the Flip function received minimal engagement from participants.
The majority of participants did not utilize this feature, explaining that
they did not have a habit of checking the symmetry of their makeup (P5: ”Using
the mirror flip would make myself look weird.”). The limited usage of the Flip
function was attributed to participants’ lack of habit in verifying the
symmetry of their makeup.

####  7.2.3. Auxiliary line Function

The evaluation of the auxiliary line function was met with appreciation by all
participants. The majority of participants (6 out of 5) considered it to be a
valuable guide for makeup application, effectively indicating the specific
areas where color should be applied (P3: ”Without the auxiliary line, I might
have inadvertently extended the makeup beyond the intended area. The auxiliary
line’s presence serves as a strong preventive measure.”). This feedback
underscores the utility of the auxiliary line in ensuring accurate and precise
makeup application.

However, a small number of participants raised concerns about potential
drawbacks associated with the auxiliary line function. They noted that the
presence of the auxiliary line could obstruct the view of the tool being used
for makeup, thus affecting their ability to accurately judge the intended area
(P1: ”The presence of the auxiliary line, particularly when applying eyeliner,
can block my view and make it challenging to determine the exact placement of
the line.”). This feedback suggests that while the auxiliary line provides
guidance, it may inadvertently hinder the visibility of certain tools and
potentially compromise precision.

Furthermore, one participant pointed out that the auxiliary line, particularly
for specific steps, should be viewed as a reference rather than a definitive
guide for drawing (P2: ”Due to the partial obstruction caused by the auxiliary
line, the eyeliner aid can only serve as a general representation rather than
an exact auxiliary line.”). This observation implies that participants had to
rely on their own judgment and adapt the use of the auxiliary line as a
general reference rather than a strict delineation.

##  8\. Discussion

This study aimed to address three research questions related to learning Xiqu
makeup: 1) the challenges associated with learning Xiqu makeup, 2) the design
of an AR-based interactive learning approach to address these challenges, and
3) the effectiveness of AR-based interactive learning compared to video
tutorials. We conducted semi-structured interviews with 12 Xiqu artists to
identify the challenges and the need for detailed and personalised makeup
instructions. Based on these findings, we developed OperARtistry, an
interactive AR-based app that provides step-by-step instructions for Xiqu
makeup novices, focusing on the eye area for initial assessment. Six
participants with varying levels of make-up experience compared OperARtistry
with traditional video tutorials. The results showed that OperARtistry enabled
participants to achieve higher quality makeup results in terms of color,
shape, completion time, effect and similarity compared to the baseline.
Moreover, We also assessed usability of OperARtistry from three perspectives:
ease of use, expected time spent, and user satisfaction, and received positive
user feedback.

###  8.1. How does interactive AR-based approach assist with Xiqu makeup
process?

Our findings show that the AR-based interactive application, OperARtistry,
helps beginners learn Xiqu makeup in three ways. Firstly, OperARtistry offers
real-time feedback and step-by-step instructions, enabling novices to
accurately follow the makeup process. In contrast, video tutorials often lack
interactivity and fail to provide detailed instructions for specific actions
required with different cosmetic products. Additionally, OperARtistry provides
personalized and detailed makeup instructions tailored to individuals’ unique
facial features, whereas other approaches typically offer generic instructions
that do not consider individual variations. Furthermore, OperARtistry’s
intuitive interface design enhances the user experience, allowing beginners to
navigate and follow instructions smoothly. In comparison, video tutorials may
lack interactive features and can be difficult to navigate. Although prior
work has explored the generation of hierarchical makeup video tutorials for
makeup learning (Truong et al., 2021a), they still lack the interactivity and
intuitive feedback provided by AR-based apps. These limitations can hinder the
learning process. Therefore, OperARtistry stands out as a superior approach in
providing interactive feedback and personalized guidance for effective Xiqu
makeup learning.

###  8.2. How AR-based interactive technologies be designed to better assist
Xiqu makeup process?

####  8.2.1. Interactive Feature Point Adjustment

Since there are thousands of people, when the user chooses the right opera
makeup and tries it on, there are some details (e.g. eyes, lips) that may not
fit well. At this point, if there is an interactive design that allows the
user to adjust the area of the feature points to make the parts fit better,
then it can make our system more personalized. The implementation of such
interactive designs can start from a 2D drawing, for example, the user can
start adjusting the position of a point in a 2D drawing with 468 feature
points and see the effect in real-time 3D.

####  8.2.2. Customization on Light Condition

Even though we provide small plugins like ”flip” and ”on/off” to help users
compare their makeup, the effect of lighting is still important. Therefore, it
is also crucial to add the function of light adjustment to the AR camera. One
possible solution is to give the user a progress bar to adjust the light so
that the light intensity or the position of the light source can be adjusted
in the materials of three.js.

####  8.2.3. Step-by-step presentation

During the evaluation phase, participants expressed concerns regarding the
absence of a visual representation of the final outcome at each step when
relying solely on AR guidance. Additionally, they highlighted a lack of
clarity regarding the current makeup application technique, which resulted in
a less seamless flow of the overall makeup process. To address these issues,
an effective solution would be to include a rendering of the end result on the
interface of each makeup step, accompanied by a demonstration of the makeup
technique for that particular step.

####  8.2.4. Selection of instructional videos

Despite careful consideration in selecting the format and content of the Xiqu
makeup tutorial videos, the video fell short in terms of precision when
compared to the actual makeup steps, and its short duration limited its
effectiveness. We could compare more interactive video instructions (e.g.
automatic hierarchical guide generation from makeup videos (Truong et al.,
2021b) to make more comprehensive discussions.

###  8.3. How to generalize the current approach from the eye to the whole
face makeup?

As eye make up is the most complex part of Xiqu make up, we have created a
prototype dedicated to eye make up instructions. Similar steps can be easily
generalized to all parts of the face since facial landmark detection includes
all areas of the face, the only part that needs to be changed is the content
of customized color modules for each step of the guidance. The challenges will
be related to personalized makeup adaptation, for example, professional Xiqu
make up should change with the shape of the person’s face and the role they
are performing, in order to achieve considerable visual effects (Liu, 1997;
Li, 2003).

###  8.4. How to apply the insights to other intangible cultural heritage
learning processes?

We started with the subject of researching Chinese traditional opera makeup,
using a similar algorithm for locating featured points on the face, which also
leads to a number of efforts to alleviate user learning time as well as
improve usability. The potential for generalizability of our work extends
beyond the realm of traditional Xiqu makeup and into a multitude of artistic
fields that involve intricate visual effects, for example, Western opera and
body painting are equally important forms of artistic expression (Corson et
al., 2019). We can easily reproduce our work on the make up learning of these
different intangible cultural heritages which also require a high level of
visual effect. However, to apply our systems to other ICHs that require
makeup, understanding the characteristics of the makeup for each type of ICH
separately is needed. Overall, these possible uses highlight the adaptability
and versatility of our work by providing a thorough and inclusive learning
environment for a variety of artistic communities and those interested in
learning more about the makeup and visual effects industries. This inclusive
approach promotes a deeper understanding and appreciation of these art forms,
while encouraging their preservation and transmission to future generations.

##  9\. Conclusion

We have presented OperARtistry, an interactive Augmented Reality (AR)-based
makeup tutorial system for learning the Chinese Xiqu makeup. Our application
provides users with a comprehensive and effective way to learn one of the most
complicated aspects of Xiqu makeup: eye makeup. To evaluate the usability of
our AR system, we conducted a user study with six participants of varying
familiarity with makeup. The study compared the effectiveness of our system
with that of existing video tutorials. The results indicate that our system
was indeed beneficial for its intended purpose. Most users who tried the
interactive tutorial agreed to some extent that the makeup application process
was easier with the assistance of AR. Moreover, the majority of users
completed the makeup application process in less time than expected when
following the interactive tutorials. Notably, a few users also took less time
than expected with the video version. Overall, our findings suggest that
interactive tutorials contribute to more effective makeup applications, as
perceived by users. Additionally, our work provides an initial exploration of
AR Xiqu makeup guidance, as we also open up more directions to explore, more
studies with more participants will be conducted in future work.

###### Acknowledgements.

We express our profound gratitude to our reviewers for their insightful
feedback and to our participants for their invaluable contributions. Our
sincere appreciation is extended particularly to Professor Man Chan and the
Division of RBM at the College of Future Technology, The Hong Kong University
of Science and Technology (Guangzhou), whose unparalleled support was
instrumental to this work.

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