# ANR: Articulated Neural Rendering for Virtual Avatars

Amit Raj 1 Julian Tanke2 James Hays1

Minh Vo3 Carsten Stoll4 Christoph Lassner3

1Georgia Tech 2University of Bonn 3Facebook Reality Labs 4Epic Games

###### Abstract

The combination of traditional rendering with neural networks in Deferred
Neural Rendering (DNR) [37] provides a compelling balance between
computational complexity and realism of the resulting images. Using skinned
meshes for rendering articulating objects is a natural extension for the DNR
framework and would open it up to a plethora of applications. However, in this
case the neural shading step must account for deformations that are possibly
not captured in the mesh, as well as alignment inaccuracies and dynamics—which
can confound the DNR pipeline. We present Articulated Neural Rendering (ANR),
a novel framework based on DNR which explicitly addresses its limitations for
virtual human avatars. We show the superiority of ANR not only with respect to
DNR but also with methods specialized for avatar creation and animation. In
two user studies, we observe a clear preference for our avatar model and we
demonstrate state-of-the-art performance on quantitative evaluation metrics.
Perceptually, we observe better temporal stability, level of detail and
plausibility. More results are available at our project page: https://anr-
avatars.github.io.

Figure 1: We present Articulated Neural Rendering (ANR), a rendering framework
capable of producing highly realistic avatars. Similar to Deferred Neural
Rendering (DNR) [37], ANR uses neural networks to convert a latent texture on
a coarse mesh (left) into an RGB image (right). Unlike DNR, which is
ineffective when the mesh geometry is inaccurate or deforms during motion, ANR
explicitly accounts for such geometric misalignment and pose-dependent
deformation.

##  1 Introduction

Figure 2: Schematic overview of the proposed framework. Given a coarse,
animated 3D body mesh, ANR produces a detailed avatar. Using rasterized IUV
images of the mesh using a weak perspective projection, we render an 8 channel
neural texture into image space. A first stage, $\mathcal{R}_{1}$, transforms
the texture into another, refined latent representation, which we combine with
the normal information. The second stage $\mathcal{R}_{2}$ uses this
information to create an RGB rendering and a foreground mask. The rendering
can extend beyond the coarse mesh, in this case we visualize only slight
refinement for painting the shirt.

Capturing realistic appearance is one of the important goals of computer
vision. Progress in 3D rendering and neural networks has led to approaches
with remarkable fidelity [21, 22, 28, 29]. These methods often use expensive
and intricate capture setups which prevent easy digitization and transfer of
the resulting models [7, 8, 11]. The recent deferred neural rendering paradigm
offers an exciting opportunity to work with inaccurate geometry and relatively
simple neural shaders while capturing complex scenes with view-dependent
effects realistically [1, 26, 37]. In a first step, the geometry is rasterized
using a neural latent texture which is then translated to an RGB image using a
convolutional network. Both, the rendering network as well as the neural
texture, are optimized to produce realistic results.

Deferred neural rendering works particularly well for rigid objects. Its
pipeline could be extended to deformable objects in a natural way: a _skinned_
mesh could be used for capturing the geometry. The rasterized neural texture
from the posed mesh could then be translated to an RGB image. While this idea
is conceptually simple, the neural network has to learn more complex
deformation-dependent effects. Furthermore, the mesh used for rendering is
usually not a perfect representation of the real geometry, leading to
alignment problems. These problems are currently not taken into account [1,
26, 37], which limits the application of DNR in scenarios with deformable
objects.

In this paper, we present Articulated Neural Rendering (ANR) to account for
these problems. ANR systematically rebuilds DNR from the neural shading model
architecture to the optimization scheme. We use ANR to tackle one of the most
challenging problems for animation: virtual human avatars. Fig. 1 shows an
example of an avatar rendered using ANR.

Our method employs a simple statistical human body model fitted to a training
video to capture the body shape statistics and 3D pose information for each
frame [38]. This body model only represents the coarse body geometry without
clothing and hair. Consequently, direct use of the DNR pipeline leads to
unrealistic and blurry results. We use keyframes from the video to learn the
static appearance encoded in the neural texture, and use the other frames to
learn the dynamic pose-conditioned rendering of the appearance. Our keyframes-
based training scheme enables the model to converge 5X faster and produces
quantitatively better avatars than DNR. We simultaneously train ANR on
multiple identities in a single model, leading to decoupling of the neural
texture and the shading model. Owing to the consistent surface
parameterization of the statistical body model, our model can leverage such
semantic correspondences to modify and mix components from multiple neural
textures, enabling virtual try-on by changing regions in the neural texture.
While our model works solely in 2D, we experimentally validate that it can
render near photorealistic and persistent 3D appearance of people with a very
small network (161M parameters). In two user studies, we demonstrate that we
not only outperform the DNR pipeline, but also several methods dedicated to
creating virtual avatars [35, 40]. Perceptually, the presented method is
temporally stable and captures fine appearance details.

Our contributions are threefold:

  * •

We propose ANR, a novel neural rendering framework, to generate high-quality
virtual avatars from coarse 3D shape and arbitrary skeletal motions. Key to
ANR is to account for geometric misalignment of the coarse body mesh and pose-
dependent deformation.

  * •

Using ANR, we present the first neural avatar model that can capture and
render multiple identities with only one set of network parameters in addition
to an identity specific neural texture map.

  * •

The explicit texture space makes it possible to edit or mix identities, which
is novel in the context of neural rendering for avatars.

For higher quality results, fine-tuning the model on a specific identity is an
option. This makes the resulting avatars directly applicable in use cases
where the range of motion is known or can be estimated well, for example for
virtual assistants or game characters.

##  2 Related Work

Among many methods to create and render articulated models, a majority of them
follow the classical pipeline of acquiring an accurate 4D geometry
reconstruction with detailed textures painted on this geometry. Using machine
learning, several recent methods have set out to perform inference mostly in
2D space, only using rough or no 3D guidance. We will discuss several
frameworks from both of these schools as well as some hybrid methods, which
are closest to our approach.

Inference in 3D Space: The Relightables [11] propose a system that captures
accurate geometry and texture using a controlled light stage. This allows for
relightable rendering of the captured identity in different environments.
Lombardi et al. [21] use a multi camera setup to determine the average texture
and deformations on a base mesh and use a neural network to generate view
specific texture to render high fidelity images from different viewpoints.
Using a similar system, Brualla et al. [27] train a network to perform
completion and super resolution of the rendered 3D model. In a single-view
regime, Alldiek et al. [2, 3] generate avatars by learning to regress accurate
geometry and texture using purely synthetic data. Zhi et al. [44] estimate
personalized avatar with fine geometry and texture by finetuning on the test
video using self-supervised losses. DeepCap [12] captures accurate geometry
from monocular video by predicting a parameterized human configuration and
deformation model. Our approach also uses monocular video to capture a digital
avatar. However, instead of deforming and refining the avatar geometry
parameterized by a statistical model, we advocate for avatars with high-
capacity texture that compensates for such geometric inaccuracies (such as
clothing and hairs) for arbitrary body pose and view-point rendering.

Inference in 2D Space: Meanwhile, specific architectures are designed for
motion re-targeting, novel view synthesis and identity transfer, which
primarily use only pixel and pose information [4, 6, 25, 36, 43]. Neverova et
al. [30] use DensePose for novel viewpoint synthesis, which is limited by the
DensePose body coverage and accuracy. [31] propose a semi-parametric approach
which uses a combination of previously captured RGB(D) images and neural
rendering to infer novel views in an approach similar to image based
rendering. The works by [24, 18] focus on pose conditioned image generation of
people, but with lower resolution. Grigorev et al. [10] solve the novel view
synthesis problem by formulating it as texture inpainting in DensePose UV
space. SwapNet [32] learns to transfer clothing information by disentangling
the notion of pose and clothing without being identity specific. Human
appearance transfer [42] learns to generate novel views and transfer
identities by performing human parsing and 3D shape and pose fitting. We
generate 3D textured avatars, enabling all these tasks with no additional
guiding signals or changes in a single framework.

Hybrid Approaches: The DNR framework [37] uses a mostly rigid mesh and a
neural texture to translate the rendering result into an image. We detail this
approach in Sec. 3.1 and reformulate it to account for handling fully
articulated objects. Textured neural avatars [35] present a framework to learn
neural avatars in an end-to-end manner from multiview data. Unlike this work,
we leverage the reconstructed geometry instead of noisy DensePose
correspondences to generate the UV coordinates for every pixel, enabling us to
maintain better texture consistency across viewpoints. Our work is also
related to the Liquid Warping Gan [20] which performs appearance transfer and
motion retargeting in a single network. However, our framework provides
explicit access to the learned texture allowing for fine grained edits of
appearance. Additionally, our framework uses a lower number of parameters, and
thus can be trained at a higher resolution. Neural rendering and reenactment
[19] trains a network to translate from 3D pose to image. However, their
framework involves capturing a rigged template mesh for every individual and
requiring additional depth and body part information. Recently, implicit
representations with impressive geometry reconstruction of clothed humans
reconstruction from a single image are becoming popular [14, 34]. We
distinguish ourselves from these methods by generating clothing and body
deformations in the rendering stage while using a simple parametric body model
to fit the body pose and shape.

##  3 Approach

Figure 3: Images generated by ANR on a challenging animation scenario with
clothing deformations. We successfully synthesize images of such deformations
and regions outside of the body mesh. As highlighted in the figure, region
coverage as well as shading are pose dependent. The example frames are unseen
poses for this identity and rendering model. Additionally, the model is able
to temporally interpolate between this pose and others and adjust the
simulated clothing accordingly. We refer to the supplementary video for a
demonstration of temporal stability.

Articulated Neural Rendering (ANR) can generate highly detailed
representations of articulated objects. Unlike traditional rendering pipelines
which use a high resolution mesh and detailed RGB texture for this purpose, we
use a low resolution mesh but a high-_dimensional_ neural texture to render
its detailed RGB appearance from novel views using a neural network. Fig. 2
shows an outline of the proposed framework. In the following, we first present
an overview of DNR [37] before presenting our novel ANR framework and its
training scheme.

###  3.1 Preliminary: Deferred Neural Rendering

DNR [37] translates high dimensional neural latent textures on traditional
meshes into RGB images with a neural translator network. Concretely, let
$\mathcal{T}$ be a high-dimensional neural texture (a tensor of shape $W\times
H\times C$) and let $\mathcal{R}$ be the neural rendering model converting a
neural image $\mathbf{I}^{\mathrm{uv}}$ to RGB color. DNR optimizes

$\mathcal{T}^{*},\mathcal{R}^{*}=\underset{\mathcal{T},\mathcal{R}}{\mathrm{argmin}}\sum||\mathbf{I}-\mathcal{R}(\mathcal{T},\mathbf{I}^{\mathrm{uv}})||$
(1)

on all training images $\mathbf{I}$ of the same object. The neural image
$\mathbf{I}^{\mathrm{uv}}$ is the result of rasterizing the mesh to image
space with the appropriate camera parameters and configuration and texturing
it with the neural texture $\mathcal{T}$. The model is fully defined with the
optimized texture $\mathcal{T}^{*}$ and the optimized neural rendering model
$\mathcal{R}^{*}$. DNR uses a U-Net architecture [33] for implementing
$\mathcal{R}$ and a standard gradient descent based optimization with the ADAM
optimizer [17].

###  3.2 Articulated Neural Rendering

While DNR is conceptually powerful, it requires accurate 3D geometry to learn
view dependent appearance information. Such an assumption is difficult to make
in practice, especially for articulated, clothed human appearance, whose shape
is often represented by a coarse statistical body shape model [23] (see Fig.
3). We address this problem in the rendering pipeline, while retaining the
ability to work with a coarse, animated mesh to (1) maintain the high
rendering speed and (2) be able to optimize the final appearance generation in
the neural network. Consequently, we re-visit the neural rendering component,
$\mathcal{R}$.

Our first observation is that $\mathcal{R}$ not only has to paint the texture
inside regions of the neural image $\mathbf{I}^{\mathrm{uv}}$ but also beyond
its boundaries due to the use of the coarse mesh. The network should also be
aware of the extent to which it needs to paint outside the boundaries of the
rasterized mesh. We address both problems by adding a second prediction: an
extra single-channel soft mask $\mathbf{M}\in[0,1]$. The predicted mask is
used to blend the generated avatar with the ground-truth background image for
training. To prevent the model from predicting a degenerated zero mask (which
would minimize the loss to zero), we provide supervision for the mask from an
automatic image matting method [9]. Note that while training on the pre-
segmented image is another option, this approach is sensitive to erroneous
segmentation which prevents the generated images to grow beyond their input
coarse body boundaries. Comparing the blended image with the ground truth
image allows gradients to flow to the mask which in turn makes it potentially
better than the supervised input mask.

While this addresses the immediate problem of generating content outside of
the true geometry silhouette, it leaves geometric details and pose-dependent
rendering untouched. We notice that naively increasing the capacity of the
U-Net does not improve generation quality (see Tab. 2). Furthermore, we
observe that the model cannot consistently render local geometry—a problem
that increasingly emerges in the articulated setting when geometry is
animated. We address problems with the geometry details and the pose-dependent
effects at the same time by splitting the neural rendering network in two
stages: $\mathcal{R}_{1}$ and $\mathcal{R}_{2}$. Both components are shallow
U-Nets and produce renderings at original image resolution. We can inject the
normal information into the rendering process by concatenating the rendered
normal image and output of $\mathcal{R}_{1}$ to the input of
$\mathcal{R}_{2}$. We enforce an additional RGB loss on the first three output
channels of $\mathcal{R}_{1}$ to aid in convergence. The ANR model is defined
as

$\displaystyle\hat{\mathbf{M}},\hat{\mathbf{I}},\hat{\mathbf{J}}=\mathcal{R}_{2}(\mathcal{R}_{1}(\mathcal{T},\mathbf{I}^{\mathrm{uv}}),\mathbf{I}^{\mathrm{norm}}),$
(2)

where $\hat{\mathbf{J}}$ are the first three channels from the result of
$\mathcal{R}_{1}$ and $I^{norm}$ is the rasterized normal image. This model
has the necessary capacity and the necessary outputs for handling the
articulated neural rendering problem.

###  3.3 Loss Functions and Regularization Scheme

With the higher requirements for stability and level of detail and
deformations in the articulated setting, we find that using a simple
$\ell_{1}$ loss is insufficient (see Tab. 2). Furthermore, we observe that it
deteriorates performance as the training progresses: once the model learns to
reproduce the rough appearance, inaccuracies in tracking and alignment of the
mesh have an increasingly negative impact (see Fig. 5). We use adversarial
learning and feature loss computation to guide the model to generate realistic
and accurate appearance without having to rely on accurate registration. Our
loss function is a weighted sum of the photometric loss $\mathcal{L}_{p}$,
feature loss $\mathcal{L}_{feat}$, mask loss $\mathcal{L}_{mask}$, adversarial
loss $\mathcal{L}_{adv}$, and total variation loss $\mathcal{L}_{tv_{i}}$.
Note that while the rasterization is non-differentiable, ANR is fully
differentiable given the precomputed rasterized UV lookup to paint
$I^{\mathrm{uv}}$ from the neural texture $\mathcal{T}$.

Pixel Loss: We enforce an $\ell_{1}$ loss between the generated RGB and ground
truth images as

$\mathcal{L}_{p}(\hat{\mathbf{M}},\hat{\mathbf{I}},\hat{\mathbf{J}};\mathbf{M},\mathbf{I})=\hat{\mathbf{M}}||\hat{\mathbf{J}}-\mathbf{I}||+\hat{\mathbf{M}}||\hat{\mathbf{I}}-\mathbf{I}||,$
(3)

where $\hat{\mathbf{J}}$ are the first three channels of the result from
$\mathcal{R}_{1}$.
Mask Loss: Similarly, we use a Binary Cross Entropy loss for the mask

$\mathcal{L}_{mask}(\hat{\mathbf{M}};\mathbf{M})=\mathrm{BCE}(\hat{\mathbf{M}},\mathbf{M})$
(4)

For all following loss definitions, we introduce the shorthand
$\hat{\mathbf{I}}^{\prime}$ for the blended version of the generated output
with the scene background $\mathbf{B}$ given the predicted mask:
$\hat{\mathbf{I}}^{\prime}=\hat{\mathbf{M}}\hat{\mathbf{I}}+(1-\hat{\mathbf{M}})\mathbf{B}$.
Feature Loss: To increase sharpness in the rendered outputs, we enforce a
feature loss [16]:

$\mathcal{L}_{feat}(\hat{\mathbf{I}},\hat{\mathbf{M}};\mathbf{I})=\sum_{j}w_{j}||\phi_{j}(\hat{\mathbf{I}}^{\prime})-\phi_{j}(\mathbf{I})||,$
(5)

where $\phi_{j}$ are features from the $j$-th layer of a pretrained feature
extractor and $w_{j}$ is the weight associated with the $j$-th feature loss
term.
TV Loss: Since the texture is optimized over multiple frames, slight
misalignments can cause the learned texture to have certain high frequency
artifacts, especially for small regions such as face and hands. To encourage
smooth generated images, we enforce a total-variation loss on both, the mask
and the generated image.

$\mathcal{L}_{tv}(\hat{\mathbf{I}},\hat{\mathbf{M}})=\beta_{I}TV(\hat{\mathbf{I}}^{\prime})+\beta_{m}TV(\hat{\mathbf{M}}),$
(6)

where $\beta_{I}$ and $\beta_{m}$ are weights associated with Image and mask
TV loss respectively (see supp. mat. for a detailed definition of this loss).
Adversarial Loss: Adversarial training [13] is well-suited for enforcing
realism of the results and encourage the coarse body mask to extend to the
true geometry silhouette. To encourage a high level of detail in the results,
we use a multiscale discriminator $\mathcal{D}$ [41] and express the loss as

$\mathcal{L}_{adv}(\hat{\mathbf{I}})=\mathcal{D}(\hat{\mathbf{I}}^{\prime},1).$
(7)

Total loss: The loss used to train $\mathcal{R}$ is then given as

$\mathcal{L}_{total}=\sum_{i\in\mathfrak{L}}\lambda_{i}\mathcal{L}_{i}$ (8)

where $\mathfrak{L}=\\{p,feat,mask,adv,tv\\}$ is the set of all losses.

###  3.4 Optimization

Despite the extended set of losses and weight balancing, we find that for
clothing with large surface deformations, the model starts averaging fine
textures in areas of high deformation. To mitigate this problem, we propose a
_split optimization_ strategy. Specifically, we use a small set of keyframes
$\\{\mathcal{K}_{i}\\}_{i=1}^{n}$, capturing static salient appearances in the
video, to learn the neural texture $\mathcal{T}$ and use the other frames to
dynamically blend between the appearances in the keyframes in the neural
renderer $\mathcal{R}$.

We select keyframes by greedily adding a small number of frames in the video
sequence such that their cumulative silhouette coverage is maximized. This
ensures that the entire pose-space is adequately covered to capture texture
details at all locations on the body. Using a smaller number of frames (less
than 10% of training frames) reduces the texture averaging. During training,
we alternate between training the identity specific neural texture from the
keyframes and the rendering network from the remaining frames. Empirically, we
observe that this optimization scheme helps the translator network converge up
to 5X faster and produces quantitatively better avatars (see Tab. 1). Overall,
our optimization alternates between the following two objectives

$\displaystyle\underset{\mathcal{R}}{\mathrm{argmin}}\sum\mathcal{L}_{total}(\mathbf{I},\mathbf{M},\mathcal{R}(\mathcal{T},\mathbf{I}^{\mathrm{uv}},\mathbf{I}^{\mathrm{norm}})),$
(9)
$\displaystyle\underset{\mathcal{T},\mathcal{R}}{\mathrm{argmin}}\sum_{k\in\mathcal{K}}\mathcal{L}_{total}(\mathbf{I}_{k},\mathbf{M}_{k},\mathcal{R}(\mathcal{T},\mathbf{I}_{k}^{\mathrm{uv}},\mathbf{I}_{k}^{\mathrm{norm}})).$
(10)

Note that while Eq. 9 is optimized for all the images, Eq. 10 is applied only
to the keyframes to mitigate the geometric misalignment of the coarse body
mesh.

Figure 4: Comparisons for novel pose and view synthesis with Textured Neural
Avatar (TNA) [35], vid2vid (V2V) [40] and Deferred Neural Renderer (DNR) [37].
Our method (ANR) preserves the facial details better compared to competing
methods. Additionally, our method is able to capture view dependent structures
like hairline and clothing overhang more accurately and leads to more
realistic and believable shading.

Multi-instance Training. We further extend the training scheme beyond a single
capture instance. Since we use the same statistical mesh regardless of
identity, allowing us to capture identity information only in the neural
texture, our framework can naturally train on multiple identities
simultaneously in a single network. During optimization, we select an identity
for every step at random and use an identity-specific neural texture
$\mathcal{T}_{i}$ for the respective identity for the update step. The multi-
instance training offers the additional benefit that the neural rendering
component $\mathcal{R}$ generalizes beyond a single identity and can be used
to render new identities by only using a novel neural texture $\mathcal{T}$.

Regularization. To improve generalization, we additionally employ two training
regularization schemes. First, we use the same initialization of $\mathcal{T}$
for all identities by uniformly sampling in $[-1,1]$. Since $\mathcal{R}$ has
much larger capacity than $\mathcal{T}$, this strategy prevents the model from
using the distinct noise patterns in each randomly initialized $\mathcal{T}$
to memorize the identity and thus encourages decoupling of $\mathcal{T}$ and
$\mathcal{R}$. Second, we perturb the input sampling grid with a uniform
samples from $[-0.02,0.02]$ and clamp the resulting grid back to $[-1,1]$.
This form of data augmentation prevents the network from relying strictly on
the spatial extent of the sampling grid as the ground truth human silhouette
can exist outside the rasterized coarse body model.

##  4 Experiments

We use the ANR pipeline to build a realistic virtual human avatar pipeline: we
assume a setting where a user performs a recording of themselves with accurate
tracking, in which his/her full appearance is visible, to create an avatar
model. To ease the tracking, we capture 6 videos using a Kinect V2 where the
depth data is only used for tracking. Each video is about 3$\sim$5 min long.
We obtain a coarse mesh in real-time by solving an inverse kinematic problem
to fit the posed body shape to the 3D point cloud similar to [39], making use
of additional detected body keypoints [5]. Our dataset is harder than the
previously released iPer dataset [36] as our actors are not centered and are
free to move anywhere in the frame. As parametric body model, we use a
blendshape-based, SMPL-like [23] human model to provide the coarse mesh
structure. The model is coarse and has only 1831 vertices and 3658 faces; the
skeletal rig has 74 joints.

###  4.1 Implementation Details

We use a variant of Pix2Pix [15, 41] for both $\mathcal{R}_{1}$ and
$\mathcal{R}_{2}$ and train the model on $1024\times 1024$ image resolution.
The images are normalized to the range [-1,1]. Each identity is encoded in a
$256\times 256\times 8$ neural texture. For each recorded sequence, we use the
first 1500 frames to train $\mathcal{R}$ and about 150 key frames to train
$\mathcal{T}$. The remaining images are used as test set. We augment the data
with random cropping and random rescaling by a factor $f\sim[0.5,1.25]$.

###  4.2 Evaluation

Table 1: Results of novel pose synthesis of avatars learned using different
methods. Our model is trained on all identities simultaneously.

| SSIM $\uparrow$ | FLIP $\downarrow$ | LPIPS $\downarrow$ | rIPFIP $\uparrow$ | mFID $\downarrow$ | User Study
---|---|---|---|---|---|---
V2V | 0.9252 | 0.0363 | 0.0703 | - | 140 | 8%
TNA | 0.9366 | 0.0323 | 0.1198 | -2.6% | 150 | 3%
DNR | 0.9398 | 0.0342 | 0.0918 | 7.7% | 92 | 9%
ANR | 0.9738 | 0.0289 | 0.0508 | 18.6% | 74 | 81.6%

Baseline and Metric: We include a comparison with two baselines: Textured
Neural Avatar (TNA) [35] and vid2vid (V2V) [40]. These methods are
fundamentally different and span the space of 2D (V2V) and 3D (TNA) inference
approaches, whereas we aim to find a middle ground. We also present
comparisons to baseline DNR [37], trained with additional feature losses for a
fairer comparison. Fig. 4 shows these comparisons. Evidently, ANR preserves
the facial details better compared to competing methods. Additionally, it is
able to capture view dependent structures like hairline and clothing overhang
more accurately and leads to more realistic and believable shading. We also
quantify these renderings using the standard SSIM, LPIPS, FLIP _supervised
metrics_ on held out test set frames, and the mFID _unsupervised metric_ on
human-figure-only avatars in novel poses. Tab. 1 shows these comparisons. Our
model outperforms the competing approaches on these benchmarks by a notable
margin on all metrics.

Figure 5: Qualitative ablation study for each term in the loss function and
the normal channels on a multi-identity model. The first four columns do not
use normal information: (a) Only pixel loss; (b) Pixel and feature losses; (c)
Pixel, feature, and mask losses; (d) Pixel, feature, mask, and TV losses; (e)
All losses + normal; (f) All loses + normal + split optimization. Notice that
the local textures and facial details are better preserved with split
optimization. Figure 6: Viewpoint generalization demonstration. The proposed
model model is robust to viewpoint variation, even for unseen poses, and shows
high level of detail. Figure 7: Avatar animation example. Any motion capture
data that can be used to animate the base mesh can be used to drive the
avatar. All avatars shown here are rendered using a single neural network.
Figure 8: Virtual Try-On example. Our method enables texture mixing by
swapping the regions of the neural texture of the desired RGB appearance. This
example validates the disentanglement of appearance and neural shading network
when ANR is trained on multiple identities. Table 2: Loss and model ablation
study for the ANR model. The model ablations marked with (-so) are run without
the suggested split optimization strategy.

| SSIM $\uparrow$ | LPIPS $\downarrow$ | FLIP $\downarrow$
---|---|---|---
_Loss ablation_
Pixel only | 0.968 | 0.086 | 0.029
Pixel+feat | 0.966 | 0.065 | 0.033
Pixel+feat+TV | 0.963 | 0.064 | 0.032
_Model ablations_
1 stage(-so) | 0.962 | 0.070 | 0.036
1 stage | 0.965 | 0.063 | 0.034
2 stage(-so) | 0.968 | 0.058 | 0.032
Ours | 0.974 | 0.050 | 0.028

Ablation Study: To quantify the effectiveness of the proposed improvements, we
run two ablation studies. Fig. 5 shows the rendered results with different
loss terms removed. We notice that without the mask and feature loss (Pixel
only), the model produces unrealistically “fat” or “thin” avatars. The feature
loss (Pixel+feat) improves the visual quality. Adding the normals improves the
level of detail in the reconstruction and aids in reasoning about self
occlusion and temporal consistency (shown in the supplementary video), split
optimization drastically improves the level of detail. Note the jump in
perceptual quality of the rendered face using the split-optimization scheme.
Lastly, we show that our two-stage neural render with intermediate normal
injection outperforms the single-stage approach with the same capacity,
validating our network design choice. This trend is quantitatively confirmed
in Tab. 2.

User Study: While SSIM, LPIPS, or FLIP are the most widely-used metrics for
generative tasks, they are merely proxy metrics which do not pay attention to
salient regions (, for faces or patterns on shirts) and do not strictly
measure perceptual quality. To demonstrate the efficacy of our method, we
conduct a 4-alternative forced-choice perceptual study with 80 participants,
where users were given a choice to pick the best avatar out of the results
generated from TNA, Vid2Vid, DNR, and our ANR. Each person was presented with
20 stimuli of avatars in novel poses for 5s (see supplementary material for
details). ANR was preferred 81.6% of the time. Furthermore, to test the
photorealism of our avatars, we conducted another 2-Alternative forced choice
study where users were presented a real image and an image of our avatar in
different poses, and asked to pick the real image. Our model was able to fool
users 30% of the time (50% being random chance) in this test. This shows the
realistic rendering performance of our model.

Model Efficiency: We calculate the relative improvement in LPIPS of each
approach (x) over vid2vid (V2V) scaled by factor of improvement in number of
parameters (#p) as

$\mathrm{rIPFIP}(x)=\frac{\mathrm{LPIPS}_{v2v}-\mathrm{LPIPS}_{x}}{\mathrm{LPIPS}_{v2v}}*\frac{log(\\#p_{v2v}/\\#p_{x})}{log(\\#p_{V2V})}$

Particularly, this metric lies in (-$\infty$,1] and reaches a maximum value
for ground truth images. This metrics highlights that we benefit from our
_design choices_ compared to DNR, and not solely from differences in capacity.

##  5 Applications

We use a single ANR model to digitize and render avatars for several
applications. Please refer to the supplementary video for more examples.

Novel View Synthesis: To render the avatar from novel views, we only need to
rasterize the tracked mesh using the scene camera parameters to create the UV
lookups. The avatar can be readily generated using the neural renderer
$\mathcal{R}$. See Fig. 6 for an illustration and the supplementary video for
additional results. The viewpoint stability is unlike most image-based CNN
approaches, which often synthesize inconsistent appearance with varying
viewpoints [40].

Animation: The learned neural identity can be retargeted to any motion from a
motion capture database. Fig. 7 shows renderings of the same motion sequence
from multiple views. Importantly, our model adds vivid and realistic pose-
dependent deformation to the rendered avatar, which is not possible for other
methods using skinned, but coarse meshes [14]. Fig. 3 provides a detailed view
of the pose-dependent deformation appearance generation.

Replacement of Textures / Virtual Try-On: The learned neural texture is not
directly interpretable. However, for two identities trained on the same neural
rendering network, we can swap parts of the neural volume to generate
identities with swapped faces/clothing items, as shown in Fig. 8. This is
unlike fully 3D based approaches which require detailed captures for each new
avatar [11].

##  6 Conclusion

We introduce Articulated Neural Rendering (ANR), a novel neural rendering
framework, to generate high-quality virtual avatars with arbitrary skeletal
animations and viewpoints. Key to our work is the ability to account for
geometric misalignment and pose-dependent surface deformation. Our solutions
are carefully integrated into an end-to-end learning framework with a novel
neural rendering architecture and adjusted optimization scheme. Additionally,
ANR can render multiple avatars using a single neural rendering model. Through
decoupling of texture and geometry it allows for mixing and editing of
appearance. For future work, we see potential directions in further mitigating
the impact of geometric misalignment and improving resiliency towards large
pose tracking errors as well as incorporating environmental lighting into the
rendering process.

Acknowledgement This work was done while AR, JT, and CS were at Facebook. We
thank Tiancheng Zhi and Tony Tung for help with data processing and Michael
Zollhöefer for the fruitful discussions.

## References

  * [1] Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos, Dmitry Ulyanov, and Victor Lempitsky.  Neural point-based graphics.  arXiv preprint arXiv:1906.08240, 2019.
  * [2] Thiemo Alldieck, Marcus Magnor, Bharat Lal Bhatnagar, Christian Theobalt, and Gerard Pons-Moll.  Learning to reconstruct people in clothing from a single rgb camera.  In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.
  * [3] Thiemo Alldieck, Marcus Magnor, Weipeng Xu, Christian Theobalt, and Gerard Pons-Moll.  Video based reconstruction of 3d people models.  In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018.
  * [4] Guha Balakrishnan, Amy Zhao, Adrian V Dalca, Fredo Durand, and John Guttag.  Synthesizing images of humans in unseen poses.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8340–8348, 2018.
  * [5] Zhe Cao, Gines Hidalgo, Tomas Simon, Shih-En Wei, and Yaser Sheikh.  Openpose: realtime multi-person 2d pose estimation using part affinity fields.  arXiv preprint arXiv:1812.08008, 2018.
  * [6] Caroline Chan, Shiry Ginosar, Tinghui Zhou, and Alexei A Efros.  Everybody dance now.  In Proceedings of the IEEE International Conference on Computer Vision, pages 5933–5942, 2019.
  * [7] Alvaro Collet, Ming Chuang, Pat Sweeney, Don Gillett, Dennis Evseev, David Calabrese, Hugues Hoppe, Adam Kirk, and Steve Sullivan.  High-quality streamable free-viewpoint video.  ACM Transactions on Graphics (ToG), 34(4):1–13, 2015.
  * [8] Paul Debevec, Tim Hawkins, Chris Tchou, Haarm-Pieter Duiker, Westley Sarokin, and Mark Sagar.  Acquiring the reflectance field of a human face.  In Proceedings of the 27th annual conference on Computer graphics and interactive techniques, pages 145–156, 2000.
  * [9] Ke Gong, Xiaodan Liang, Yicheng Li, Yimin Chen, Ming Yang, and Liang Lin.  Instance-level human parsing via part grouping network.  In Proceedings of the European Conference on Computer Vision (ECCV), pages 770–785, 2018.
  * [10] Artur Grigorev, Artem Sevastopolsky, Alexander Vakhitov, and Victor Lempitsky.  Coordinate-based texture inpainting for pose-guided image generation.  arXiv preprint arXiv:1811.11459, 2018.
  * [11] Kaiwen Guo, Peter Lincoln, Philip Davidson, Jay Busch, Xueming Yu, Matt Whalen, Geoff Harvey, Sergio Orts-Escolano, Rohit Pandey, Jason Dourgarian, et al.  The relightables: volumetric performance capture of humans with realistic relighting.  ACM Transactions on Graphics (TOG), 38(6):1–19, 2019.
  * [12] Marc Habermann, Weipeng Xu, Michael Zollhoefer, Gerard Pons-Moll, and Christian Theobalt.  Deepcap: Monocular human performance capture using weak supervision.  In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, jun 2020.
  * [13] Jingwei Huang, Justus Thies, Angela Dai, Abhijit Kundu, Chiyu Jiang, Leonidas J Guibas, Matthias Nießner, and Thomas Funkhouser.  Adversarial texture optimization from rgb-d scans.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1559–1568, 2020.
  * [14] Zeng Huang, Yuanlu Xu, Christoph Lassner, Hao Li, and Tony Tung.  Arch: Animatable reconstruction of clothed humans, 2020.
  * [15] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros.  Image-to-image translation with conditional adversarial networks.  In CVPR, 2017.
  * [16] Justin Johnson, Alexandre Alahi, and Li Fei-Fei.  Perceptual losses for real-time style transfer and super-resolution.  In European conference on computer vision, pages 694–711. Springer, 2016.
  * [17] Diederik P Kingma and Jimmy Ba.  Adam: A method for stochastic optimization.  arXiv preprint arXiv:1412.6980, 2014.
  * [18] Christoph Lassner, Gerard Pons-Moll, and Peter V Gehler.  A generative model of people in clothing.  In Proceedings of the IEEE International Conference on Computer Vision, pages 853–862, 2017.
  * [19] Lingjie Liu, Weipeng Xu, Michael Zollhoefer, Hyeongwoo Kim, Florian Bernard, Marc Habermann, Wenping Wang, and Christian Theobalt.  Neural rendering and reenactment of human actor videos, 2018.
  * [20] Wen Liu, Zhixin Piao, Min Jie, Wenhan Luo, Lin Ma, and Shenghua Gao.  Liquid warping gan: A unified framework for human motion imitation, appearance transfer and novel view synthesis.  In The IEEE International Conference on Computer Vision (ICCV), 2019\.
  * [21] Stephen Lombardi, Jason Saragih, Tomas Simon, and Yaser Sheikh.  Deep appearance models for face rendering.  ACM Transactions on Graphics (TOG), 37(4):1–13, 2018.
  * [22] Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh.  Neural volumes: Learning dynamic renderable volumes from images.  ACM Transactions on Graphics (TOG), 38(4):65, 2019.
  * [23] Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J. Black.  SMPL: A skinned multi-person linear model.  ACM Trans. Graphics (Proc. SIGGRAPH Asia), 2015.
  * [24] Liqian Ma, Xu Jia, Qianru Sun, Bernt Schiele, Tinne Tuytelaars, and Luc Van Gool.  Pose guided person image generation.  In Advances in Neural Information Processing Systems, pages 406–416, 2017.
  * [25] Liqian Ma, Qianru Sun, Stamatios Georgoulis, Luc Van Gool, Bernt Schiele, and Mario Fritz.  Disentangled person image generation.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 99–108, 2018.
  * [26] Ricardo Martin-Brualla, Rohit Pandey, Sofien Bouaziz, Matthew Brown, and Dan B Goldman.  Gelato: Generative latent textured objects.  In ECCV, 2020.
  * [27] Ricardo Martin-Brualla, Rohit Pandey, Shuoran Yang, Pavel Pidlypenskyi, Jonathan Taylor, Julien Valentin, Sameh Khamis, Philip Davidson, Anastasia Tkach, Peter Lincoln, et al.  Lookingood: enhancing performance capture with real-time neural re-rendering.  arXiv preprint arXiv:1811.05029, 2018.
  * [28] Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng.  Nerf: Representing scenes as neural radiance fields for view synthesis, 2020.
  * [29] Koki Nagano, Huiwen Luo, Zejian Wang, Jaewoo Seo, Jun Xing, Liwen Hu, Lingyu Wei, and Hao Li.  Deep face normalization.  ACM Transactions on Graphics (TOG), 38(6):1–16, 2019.
  * [30] Natalia Neverova, Riza Alp Guler, and Iasonas Kokkinos.  Dense pose transfer.  In Proceedings of the European conference on computer vision (ECCV), pages 123–138, 2018.
  * [31] Rohit Pandey, Anastasia Tkach, Shuoran Yang, Pavel Pidlypenskyi, Jonathan Taylor, Ricardo Martin-Brualla, Andrea Tagliasacchi, George Papandreou, Philip Davidson, Cem Keskin, et al.  Volumetric capture of humans with a single rgbd camera via semi-parametric learning.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 9709–9718, 2019.
  * [32] Amit Raj, Patsorn Sangkloy, Huiwen Chang, James Hays, Duygu Ceylan, and Jingwan Lu.  Swapnet: Image based garment transfer.  In European Conference on Computer Vision, pages 679–695. Springer, 2018.
  * [33] Olaf Ronneberger, Philipp Fischer, and Thomas Brox.  U-net: Convolutional networks for biomedical image segmentation.  In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer, 2015.
  * [34] Shunsuke Saito, Tomas Simon, Jason Saragih, and Hanbyul Joo.  Pifuhd: Multi-level pixel-aligned implicit function for high-resolution 3d human digitization.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 2020.
  * [35] Aliaksandra Shysheya, Egor Zakharov, Kara-Ali Aliev, Renat Bashirov, Egor Burkov, Karim Iskakov, Aleksei Ivakhnenko, Yury Malkov, Igor Pasechnik, Dmitry Ulyanov, et al.  Textured neural avatars.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2387–2397, 2019.
  * [36] Chenyang Si, Wei Wang, Liang Wang, and Tieniu Tan.  Multistage adversarial losses for pose-based human image synthesis.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 118–126, 2018.
  * [37] Justus Thies, Michael Zollhöfer, and Matthias Nießner.  Deferred neural rendering: Image synthesis using neural textures.  ACM Transactions on Graphics (TOG), 38(4):1–12, 2019.
  * [38] Aaron Walsman, Weilin Wan, Tanner Schmidt, and Dieter Fox.  Dynamic high resolution deformable articulated tracking.  In 3DV, 2017.
  * [39] Aaron Walsman, Weilin Wan, Tanner Schmidt, and Dieter Fox.  Dynamic high resolution deformable articulated tracking.  In 3D Vision, 2017.
  * [40] Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Guilin Liu, Andrew Tao, Jan Kautz, and Bryan Catanzaro.  Video-to-video synthesis.  In Advances in Neural Information Processing Systems (NeurIPS), 2018\.
  * [41] Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, and Bryan Catanzaro.  High-resolution image synthesis and semantic manipulation with conditional gans.  In CVPR, 2018.
  * [42] Mihai Zanfir, Alin-Ionut Popa, Andrei Zanfir, and Cristian Sminchisescu.  Human appearance transfer.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5391–5399, 2018.
  * [43] Bo Zhao, Xiao Wu, Zhi-Qi Cheng, Hao Liu, Zequn Jie, and Jiashi Feng.  Multi-view image generation from a single-view.  In Proceedings of the 26th ACM international conference on Multimedia, pages 383–391, 2018.
  * [44] Tiancheng Zhi, Christoph Lassner, Tony Tung, Carsten Stoll, Srinivasa G Narasimhan, and Minh Vo.  Texmesh: Reconstructing detailed human texture and geometry from rgb-d video.  In ECCV, 2020.


* [16] Dołȩga M., Féray V., On Kerov polynomials for Jack characters, Discrete Math. Theor. Computer Sci. Proc. (2013), 539–550, arXiv:1201.1806.
  * [17] Dołȩga M., Féray V., Gaussian fluctuations of Young diagrams and structure constants of Jack characters, Duke Math. J. 165 (2016), 1193–1282, arXiv:1402.4615.
  * [18] Dołȩga M., Féray V., Śniady P., Jack polynomials and orientability generating series of maps, Sém. Lothar. Combin. 70 (2013), Art. B70j, 50 pages, arXiv:1301.6531.
  * [19] Edrei A., On the generation function of a doubly infinite, totally positive sequence, Trans. Amer. Math. Soc. 74 (1953), 367–383.
  * [20] Etingof P.I., Kirillov A.A., Macdonald’s polynomials and representations of quantum groups, Math. Res. Lett. 1 (1994), 279–296, hep-th/9312103.
  * [21] Gorin V., The $q$-Gelfand–Tsetlin graph, Gibbs measures and $q$-Toeplitz matrices, Adv. Math. 229 (2012), 201–266, arXiv:1011.1769.
  * [22] Gorin V., From alternating sign matrices to the Gaussian unitary ensemble, Comm. Math. Phys. 332 (2014), 437–447, arXiv:1306.6347.
  * [23] Gorin V., Olshanski G., A quantization of the harmonic analysis on the infinite-dimensional unitary group, J. Funct. Anal. 270 (2016), 375–418, arXiv:1504.06832.
  * [24] Gorin V., Panova G., Asymptotics of symmetric polynomials with applications to statistical mechanics and representation theory, Ann. Probab. 43 (2015), 3052–3132, arXiv:1301.0634.
  * [25] Gorin V., Shkolnikov M., Multilevel Dyson Brownian motions via Jack polynomials, Probab. Theory Related Fields 163 (2015), 413–463, arXiv:1401.5595.
  * [26] Jing N.H., Józefiak T., A formula for two-row Macdonald functions, Duke Math. J. 67 (1992), 377–385.
  * [27] Johansson K., Random matrices and determinantal processes, in Mathematical Statistical Physics, Elsevier B. V., Amsterdam, 2006, 1–55, math-ph/0510038.
  * [28] Kirillov A.A., Lectures on affine Hecke algebras and Macdonald’s conjectures, Bull. Amer. Math. Soc. (N.S.) 34 (1997), 251–292, math.QA/9501219.
  * [29] Lassalle M., Jack polynomials and free cumulants, Adv. Math. 222 (2009), 2227–2269, arXiv:0802.0448.
  * [30] Lassalle M., Schlosser M., Inversion of the Pieri formula for Macdonald polynomials, Adv. Math. 202 (2006), 289–325, math.CO/0402127.
  * [31] Macdonald I.G., A new class of symmetric functions, Sémin. Lothar. Comb. 20 (1988), B20a, 131–171.
  * [32] Macdonald I.G., Symmetric functions and Hall polynomials, 2nd ed., Oxford Mathematical Monographs, The Clarendon Press, Oxford University Press, New York, 1995.
  * [33] Noumi M., Macdonald’s symmetric polynomials as zonal spherical functions on some quantum homogeneous spaces, Adv. Math. 123 (1996), 16–77, math.QA/9503224.
  * [34] Okounkov A., Binomial formula for Macdonald polynomials and applications, Math. Res. Lett. 4 (1997), 533–553, q-alg/9608021.
  * [35] Okounkov A., Infinite wedge and random partitions, Selecta Math. (N.S.) 7 (2001), 57–81, math.RT/9907127.
  * [36] Okounkov A., Olshanski G., Shifted Jack polynomials, binomial formula, and applications, Math. Res. Lett. 4 (1997), 69–78, q-alg/9608020.
  * [37] Okounkov A., Olshanski G., Asymptotics of Jack polynomials as the number of variables goes to infinity, Int. Math. Res. Not. 1998 (1998), 641–682, q-alg/9709011.
  * [38] Okounkov A., Reshetikhin N., Correlation function of Schur process with application to local geometry of a random 3-dimensional Young diagram, J. Amer. Math. Soc. 16 (2003), 581–603, math.CO/0107056.
  * [39] Okounkov A., Reshetikhin N., Random skew plane partitions and the Pearcey process, Comm. Math. Phys. 269 (2007), 571–609, math.CO/0503508.
  * [40] Olshanski G., The problem of harmonic analysis on the infinite-dimensional unitary group, J. Funct. Anal. 205 (2003), 464–524, math.RT/0109193.
  * [41] Olshanski G., Extended Gelfand–Tsetlin graph, its $q$-boundary, and $q$-B-splines, Funct. Anal. Appl. 50 (2016), 107–130, arXiv:1607.04201.
  * [42] Olshanski G., Vershik A., Ergodic unitarily invariant measures on the space of infinite Hermitian matrices, in Contemporary mathematical physics, Amer. Math. Soc. Transl. Ser. 2, Vol. 175, Amer. Math. Soc., Providence, RI, 1996, 137–175, math.RT/9601215.
  * [43] Opdam E.M., Dunkl operators, Bessel functions and the discriminant of a finite Coxeter group, Compositio Math. 85 (1993), 333–373.
  * [44] Panova G., Lozenge tilings with free boundaries, Lett. Math. Phys. 105 (2015), 1551–1586, arXiv:1408.0417.
  * [45] Range R.M., Complex analysis: a brief tour into higher dimensions, Amer. Math. Monthly 110 (2003), 89–108.
  * [46] Śniady P., Structure coefficients for Jack characters: approximate factorization property, arXiv:1603.04268.
  * [47] Vershik A.M., Kerov S.V., Characters and factor-representations of the infinite unitary group, Sov. Math. Dokl. 26 (1982), 570–574.
  * [48] Voiculescu D., Représentations factorielles de type II1 de ${\rm U}(\infty)$, J. Math. Pures Appl. 55 (1976), 1–20.


* Olweus (2012) Dan Olweus. 2012.  Cyberbullying: An overrated phenomenon?  _European Journal of Developmental Psychology_ 9, 5 (2012), 520–538.  https://doi.org/10.1080/17405629.2012.682358
  * Orne (1962) Martin T. Orne. 1962.  On the social psychology of the psychological experiment: With particular reference to demand characteristics and their implications.  _American Psychologist_ 17, 11 (1962), 776–783.  https://doi.org/10.1037/h0043424
  * Oygür et al. (2021) Iþil Oygür, Zhaoyuan Su, Daniel A. Epstein, and Yunan Chen. 2021.  The Lived Experience of Child-Owned Wearables: Comparing Children’s and Parents’ Perspectives on Activity Tracking. 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/10.1145/3411764.3445376
  * Peter and Valkenburg (2006) Jochen Peter and Patti M. Valkenburg. 2006.  Research Note: Individual Differences in Perceptions of Internet Communication.  _European Journal of Communication_ 21, 2 (2006), 213–226.  https://doi.org/10.1177/0267323105064046 Publisher: SAGE Publications Ltd.
  * Pitt et al. (2021) Caroline Pitt, Ari Hock, Leila Zelnick, and Katie Davis. 2021.  The Kids Are / Not / Sort of All Right*. In _Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems_ _(CHI ’21)_. Association for Computing Machinery, New York, NY, USA, 1–14.  https://doi.org/10.1145/3411764.3445541
  * Pizza et al. (2016) Stefania Pizza, Barry Brown, Donald McMillan, and Airi Lampinen. 2016.  Smartwatch in Vivo. In _Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems_ (San Jose, California, USA) _(CHI ’16)_. Association for Computing Machinery, New York, NY, USA, 5456–5469.  https://doi.org/10.1145/2858036.2858522
  * Prinstein and Giletta (2016) Mitchell J. Prinstein and Matteo Giletta. 2016.  Peer Relations and Developmental Psychopathology.  In _Developmental Psychopathology_. John Wiley & Sons, Ltd, New Jersey, USA, 1–53.  https://doi.org/10.1002/9781119125556.devpsy112
  * Punamäki et al. (2009) Raija-Leena Punamäki, Marjut Wallenius, Hanna Hölttö, Clas-Håkan Nygård, and Arja Rimpelä. 2009.  The Associations between Information and Communication Technology (ICT) and Peer and Parent Relations in Early Adolescence.  _International Journal of Behavioral Development_ 33, 6 (Nov. 2009), 556–564.  https://doi.org/10.1177/0165025409343828
  * Pye and Pye (1982) Lucian W. Pye and Lucian W. Pye. 1982.  Chinese Commercial Negotiating Style.
  * Razi et al. (2023) Afsaneh Razi, Ashwaq Alsoubai, Seunghyun Kim, Shiza Ali, Gianluca Stringhini, Munmun De Choudhury, and Pamela J. Wisniewski. 2023.  Sliding into My DMs: Detecting Uncomfortable or Unsafe Sexual Risk Experiences within Instagram Direct Messages Grounded in the Perspective of Youth.  _Proc. ACM Hum.-Comput. Interact._ 7, CSCW1, Article 89 (apr 2023), 29 pages.  https://doi.org/10.1145/3579522
  * Razi et al. (2020) Afsaneh Razi, Karla Badillo-Urquiola, and Pamela J. Wisniewski. 2020.  Let’s Talk about Sext: How Adolescents Seek Support and Advice about Their Online Sexual Experiences. In _Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems_ (2020-04-23) _(CHI ’20)_. Association for Computing Machinery, New York, NY, USA, 1–13.  https://doi.org/10.1145/3313831.3376400
  * Reich (2017) Stephanie M. Reich. 2017.  Connecting offline social competence to online peer interactions.  _Psychology of Popular Media Culture_ 6, 4 (2017), 291–310.  https://doi.org/10.1037/ppm0000111
  * Reich et al. (2012) Stephanie M. Reich, Kaveri Subrahmanyam, and Guadalupe Espinoza. 2012.  Friending, IMing, and hanging out face-to-face: overlap in adolescents’ online and offline social networks.  _Developmental Psychology_ 48, 2 (2012), 356–368.  https://doi.org/10.1037/a0026980
  * Rubin et al. (2015) Kenneth H. Rubin, William M. Bukowski, and Julie C. Bowker. 2015.  _Children in Peer Groups_.  John Wiley & Sons, Ltd, New Jersey, USA, Chapter 5, 1–48.  https://doi.org/10.1002/9781118963418.childpsy405 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781118963418.childpsy405
  * Rubin et al. (2011) Kenneth H. Rubin, Robert Copian, Xinyin Chen, Julie Bowker, and Kristina L. McDonald. 2011.  Peer Relationships in Childhood.  In _Developmental Science: An Advanced Textbook, 6th Ed_. Psychology Press, New York, NY, US, 519–570.
  * Rusak (2014) Gili Rusak. 2014.  The properties of Twitter network communications among teenagers. In _Proceedings of the companion publication of the 17th ACM conference on Computer supported cooperative work & social computing_ (2014-02-15) _(CSCW Companion ’14)_. Association for Computing Machinery, New York, NY, USA, 233–236.  https://doi.org/10.1145/2556420.2556505
  * Salmivalli (2010) Christina Salmivalli. 2010.  Bullying and the peer group: A review.  _Aggression and Violent Behavior_ 15, 2 (March 2010), 112–120.  https://doi.org/10.1016/j.avb.2009.08.007
  * Savvides (2002) A. Savvides. 2002.  Design of a Wearable Sensor Badge for Smart Kindergarten. In _Proceedings of the 6th IEEE International Symposium on Wearable Computers_ _(ISWC ’02)_. IEEE Computer Society, USA, 231.
  * Schoenebeck et al. (2016) Sarita Schoenebeck, Nicole B. Ellison, Lindsay Blackwell, Joseph B. Bayer, and Emily B. Falk. 2016.  Playful Backstalking and Serious Impression Management: How Young Adults Reflect on their Past Identities on Facebook. In _Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing_ (2016-02-27) _(CSCW ’16)_. Association for Computing Machinery, New York, NY, USA, 1475–1487.  https://doi.org/10.1145/2818048.2819923
  * Scholl et al. (2015) Philipp M. Scholl, Matthias Wille, and Kristof Van Laerhoven. 2015.  Wearables in the wet lab: a laboratory system for capturing and guiding experiments. In _Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing_ (Osaka, Japan) _(UbiComp ’15)_. Association for Computing Machinery, New York, NY, USA, 589–599.  https://doi.org/10.1145/2750858.2807547
  * Schouten et al. (2007) Alexander P. Schouten, Patti M. Valkenburg, and Jochen Peter. 2007.  Precursors and Underlying Processes of Adolescents’ Online Self-Disclosure: Developing and Testing an “Internet-Attribute-Perception” Model.  _Media Psychology_ 10, 2 (2007), 292–315.  https://doi.org/10.1080/15213260701375686 arXiv:https://doi.org/10.1080/15213260701375686
  * Scissors et al. (2016) Lauren Scissors, Moira Burke, and Steven Wengrovitz. 2016.  What’s in a Like? Attitudes and Behaviors around Receiving Likes on Facebook. In _Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing_ (San Francisco, California, USA) _(CSCW ’16)_. Association for Computing Machinery, New York, NY, USA, 1501–1510.  https://doi.org/10.1145/2818048.2820066
  * Scott et al. (2021) Riley A. Scott, Jaimee Stuart, and Bonnie L. Barber. 2021.  Contemporary friendships and social vulnerability among youth: Understanding the role of online and offline contexts of interaction in friendship quality.  _Journal of Social and Personal Relationships_ 38, 12 (2021), 3451–3471.  https://doi.org/10.1177/02654075211029384 arXiv:https://doi.org/10.1177/02654075211029384
  * Shah et al. (2023) Zohal Shah, Chen Chen, Gerhard Sonnert, and Philip M. Sadler. 2023.  High School Computer Gameplay and Social Media Use: Influence on CS Identity and CS Career Interests in College. In _Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 2_ (2023-03-06) _(SIGCSE 2023)_. Association for Computing Machinery, New York, NY, USA, 1347.  https://doi.org/10.1145/3545947.3576293
  * Shannon (2023) Brooke Shannon. 2023.  _Wait Until 8th_.  Wait Until 8th.  https://www.waituntil8th.org
  * Shen et al. (2021) Chenxinran Shen, Zhicong Lu, Travis Faas, and Daniel Wigdor. 2021.  The Labor of Fun: Understanding the Social Relationships between Gamers and Paid Gaming Teammates in China. In _Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems_ _(CHI ’21)_. Association for Computing Machinery, New York, NY, USA, 1–15.  https://doi.org/10.1145/3411764.3445550
  * Shih et al. (2015) Patrick C. Shih, Kyungsik Han, Erika Shehan Poole, Mary Beth Rosson, and John M. Carroll. 2015.  Use and Adoption Challenges of Wearable Activity Trackers.  _IConference 2015 proceedings_ 2015, 1-12 (2015), 1–12.
  * Shyam and Bhoria (2011) Radhey Shyam and Amit Bhoria. 2011.  Information Technology (Internet): Effects on Social Participation and Well-Being of Users.  _Journal of the Indian Academy of Applied Psychology_ 37, 1 (2011), 157–162.
  * Silva et al. (2023) Lucas M. Silva, Franceli L. Cibrian, Elissa Monteiro, Arpita Bhattacharya, Jesus A. Beltran, Clarisse Bonang, Daniel A. Epstein, Sabrina E. B. Schuck, Kimberley D. Lakes, and Gillian R. Hayes. 2023.  Unpacking the Lived Experiences of Smartwatch Mediated Self and Co-Regulation with ADHD Children. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_ _(CHI ’23)_. Association for Computing Machinery, New York, NY, USA, 1–19.  https://doi.org/10.1145/3544548.3581316
  * Singelis (1994) Theodore M. Singelis. 1994.  The Measurement of Independent and Interdependent Self-Construals.  _Personality and social psychology bulletin_ 20, 5 (1994), 580–591.
  * Smith et al. (2008) Peter K. Smith, Jess Mahdavi, Manuel Carvalho, Sonja Fisher, Shanette Russell, and Neil Tippett. 2008.  Cyberbullying: its nature and impact in secondary school pupils.  _Journal of Child Psychology and Psychiatry_ 49, 4 (2008), 376–385.  https://doi.org/10.1111/j.1469-7610.2007.01846.x _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1469-7610.2007.01846.x.
  * Somerville et al. (2019) Leah H. Somerville, Nadia Haddara, Stephanie F. Sasse, Alea C. Skwara, Joseph M. Moran, and Bernd Figner. 2019.  Dissecting ”Peer Presence” and ”Decisions” to Deepen Understanding of Peer Influence on Adolescent Risky Choice.  _Child Development_ 90, 6 (Nov. 2019), 2086–2103.  https://doi.org/10.1111/cdev.13081
  * Spies Shapiro and Margolin (2014) Lauren A. Spies Shapiro and Gayla Margolin. 2014.  Growing Up Wired: Social Networking Sites and Adolescent Psychosocial Development.  _Clinical Child and Family Psychology Review_ 17, 1 (2014), 1–18.  https://doi.org/10.1007/s10567-013-0135-1
  * Stewart et al. (2011) Miriam Stewart, Alison Barnfather, Joyce Magill-Evans, Lynne Ray, and Nicole Letourneau. 2011.  Brief report: An online support intervention: Perceptions of adolescents with physical disabilities.  _Journal of Adolescence_ 34, 4 (2011), 795–800.  https://doi.org/10.1016/j.adolescence.2010.04.007 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1016/j.adolescence.2010.04.007
  * Sticca and Perren (2013) Fabio Sticca and Sonja Perren. 2013.  Is Cyberbullying Worse than Traditional Bullying? Examining the Differential Roles of Medium, Publicity, and Anonymity for the Perceived Severity of Bullying.  _Journal of Youth and Adolescence_ 42, 5 (May 2013), 739–750.  https://doi.org/10.1007/s10964-012-9867-3
  * Subrahmanyam and Smahel (2011) Kaveri Subrahmanyam and David Smahel. 2011.  _Digital Youth: The Role of Media in Development_.  Springer, New York, NY.  https://doi.org/10.1007/978-1-4419-6278-2
  * Subrahmanyam et al. (2006) Kaveri Subrahmanyam, David Smahel, and Patricia Greenfield. 2006.  Connecting developmental constructions to the internet: identity presentation and sexual exploration in online teen chat rooms.  _Developmental Psychology_ 42, 3 (2006), 395–406.  https://doi.org/10.1037/0012-1649.42.3.395
  * Sun et al. (2020) Di ”Chelsea” Sun, Vaishnavi Melkote, and Ahmed Sabbir Arif. 2020.  Exploratory Study of Young Children’s Social Media Needs and Requirements. In _Proceedings of the 2020 ACM Interaction Design and Children Conference: Extended Abstracts_ _(IDC ’20)_. Association for Computing Machinery, New York, NY, USA, 332–337.  https://doi.org/10.1145/3397617.3397836
  * Sun et al. (2021a) Kaiwen Sun, Carlo Sugatan, Tanisha Afnan, Hayley Simon, Susan A. Gelman, Jenny Radesky, and Florian Schaub. 2021a.  “They See You’re a Girl if You Pick a Pink Robot with a Skirt”: A Qualitative Study of How Children Conceptualize Data Processing and Digital Privacy Risks. In _Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems_ _(CHI ’21)_. Association for Computing Machinery, New York, NY, USA, 1–34.  https://doi.org/10.1145/3411764.3445333
  * Sun et al. (2021b) Kaiwen Sun, Yixin Zou, Jenny Radesky, Christopher Brooks, and Florian Schaub. 2021b.  Child Safety in the Smart Home: Parents’ Perceptions, Needs, and Mitigation Strategies.  _Proceedings of the ACM on Human-Computer Interaction_ 5 (2021), 471:1–471:41.  Issue CSCW2. https://doi.org/10.1145/3479858
  * Tang et al. (2022) Ningjing Tang, Lei Tao, Bo Wen, and Zhicong Lu. 2022.  Dare to Dream, Dare to Livestream: How E-Commerce Livestreaming Empowers Chinese Rural Women. In _Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems_ (New Orleans, LA, USA) _(CHI ’22)_. Association for Computing Machinery, New York, NY, USA, Article 297, 13 pages.  https://doi.org/10.1145/3491102.3517634
  * Temitope and Christy (2015) Bankole Emmanuel Temitope and Ogunsakin Funmi Christy. 2015.  Influence of Peer Group on Academic Performance of Secondary School Students in Ekiti State.  _International journal of innovative research and development_ 4 (2015), 324–331.  https://api.semanticscholar.org/CorpusID:143363193
  * Triandis (2018) Harry C. Triandis. 2018.  _Individualism and Collectivism_.  Routledge, New York, USA.
  * Troop-Gordon (2017) Wendy Troop-Gordon. 2017.  Peer victimization in adolescence: The nature, progression, and consequences of being bullied within a developmental context.  _Journal of Adolescence_ 55 (Feb. 2017), 116–128.  https://doi.org/10.1016/j.adolescence.2016.12.012
  * Vaizman et al. (2017) Yonatan Vaizman, Katherine Ellis, and Gert Lanckriet. 2017.  Recognizing Detailed Human Context in the Wild from Smartphones and Smartwatches.  _IEEE pervasive computing_ 16, 4 (2017), 62–74.
  * Valkenburg and Peter (2001) Patti M. Valkenburg and Jochen Peter. 2001.  Online Communication Among Adolescents: An Integrated Model of Its Attraction, Opportunities, and Risks.  _Journal of Adolescent Health_ 48, 2 (2001), 121–127.  https://doi.org/10.1016/j.jadohealth.2010.08.020 Publisher: Elsevier.
  * Valkenburg and Peter (2007a) Patti M. Valkenburg and Jochen Peter. 2007a.  Internet Communication and Its Relation to Well-Being: Identifying Some Underlying Mechanisms.  _Media Psychology_ 9, 1 (2007), 43–58.  https://doi.org/10.1080/15213260709336802
  * Valkenburg and Peter (2007b) Patti M. Valkenburg and Jochen Peter. 2007b.  Preadolescents’ and adolescents’ online communication and their closeness to friends.  _Developmental Psychology_ 43, 2 (2007), 267–277.  https://doi.org/10.1037/0012-1649.43.2.267
  * Veenstra and Laninga-Wijnen (2023) René Veenstra and Lydia Laninga-Wijnen. 2023.  The prominence of peer interactions, relationships, and networks in adolescence and early adulthood.  In _APA handbook of adolescent and young adult development._ American Psychological Association, Washington, DC, US, 225–241.  https://doi.org/10.1037/0000298-014
  * Vishkaie (2018) Rojin Vishkaie. 2018.  Can Wearable Technology Improve Children’s Creativity?. In _Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers_ _(UbiComp ’18)_. Association for Computing Machinery, New York, NY, USA, 279–282.  https://doi.org/10.1145/3267305.3267564
  * Volk et al. (2015) Anthony A. Volk, Andrew V. Dane, Zopito A. Marini, and Tracy Vaillancourt. 2015.  Adolescent Bullying, Dating, and Mating: Testing an Evolutionary Hypothesis.  _Evolutionary Psychology_ 13, 4 (Dec. 2015), 1474704915613909.  https://doi.org/10.1177/1474704915613909 Publisher: SAGE Publications Inc.
  * Wang et al. (2012) Jing Wang, Ronald J. Iannotti, and Jeremy W. Luk. 2012.  Patterns of adolescent bullying behaviors: Physical, verbal, exclusion, rumor, and cyber.  _Journal of School Psychology_ 50, 4 (2012), 521–534.  https://doi.org/10.1016/j.jsp.2012.03.004
  * Wang et al. (2011) Jin-Liang Wang, Linda A. Jackson, and Da-Jun Zhang. 2011.  The mediator role of self-disclosure and moderator roles of gender and social anxiety in the relationship between Chinese adolescents’ online communication and their real-world social relationships.  _Computers in Human Behavior_ 27, 6 (2011), 2161–2168.  https://doi.org/10.1016/j.chb.2011.06.010
  * Wang et al. (2023) Xuhui Wang, Mohammad Alauddin, Abaid Ullah Zafar, Qilin Zhang, Tanveer Ahsan, and Zapan Barua. 2023.  WeChat Moments Among International Students: Building Guanxi Networks in China.  _Journal of Global Information Technology Management_ 26, 1 (Jan. 2023), 47–76.  https://doi.org/10.1080/1097198X.2023.2166752
  * Wang and Mainwaring (2008) Yang Wang and Scott D. Mainwaring. 2008.  Human-Currency Interaction: Learning from Virtual Currency Use in China. In _Proceedings of the SIGCHI Conference on Human Factors in Computing Systems_ _(CHI ’08)_. Association for Computing Machinery, New York, NY, USA, 25–28.  https://doi.org/10.1145/1357054.1357059
  * Wikipedia (2023) Wikipedia. 2023.  Chinese City Tier System.  https://en.wikipedia.org /w/index.php?title=Chinese_city_tier_system&oldid=1169815126.
  * Williams et al. (2003) Morris Williams, Owain Jones, and Constance Fleuriot. 2003.  Wearable Computing and the Geographies of Urban Childhood: Working with Children to Explore the Potential of New Technology. In _Proceedings of the 2003 Conference on Interaction Design and Children_ _(IDC ’03)_. Association for Computing Machinery, New York, NY, USA, 111–118.  https://doi.org/10.1145/953536.953552
  * Xiao et al. (2022) Sijia Xiao, Coye Cheshire, and Niloufar Salehi. 2022.  Sensemaking, Support, Safety, Retribution, Transformation: A Restorative Justice Approach to Understanding Adolescents’ Needs for Addressing Online Harm. In _CHI Conference on Human Factors in Computing Systems_ (2022-04-27). ACM, New Orleans LA USA, 1–15.  https://doi.org/10.1145/3491102.3517614
  * Yang et al. (2011) Jiang Yang, Mark S. Ackerman, and Lada A. Adamic. 2011.  Virtual Gifts and Guanxi: Supporting Social Exchange in a Chinese Online Community. In _Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work_ _(CSCW ’11)_. Association for Computing Machinery, New York, NY, USA, 45–54.  https://doi.org/10.1145/1958824.1958832
  * Yang (2016) Mayfair Mei-hui Yang. 2016.  _Gifts, Favors, and Banquets: The Art of Social Relationships in China_.  Cornell University Press, Ithaca, N.Y.
  * Yarosh et al. (2016a) Svetlana Yarosh, Elizabeth Bonsignore, Sarah McRoberts, and Tamara Peyton. 2016a.  YouthTube: Youth Video Authorship on YouTube and Vine. In _Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing_ _(CSCW ’16)_. Association for Computing Machinery, New York, NY, USA, 1423–1437.  https://doi.org/10.1145/2818048.2819961
  * Yarosh et al. (2016b) Svetlana Yarosh, Elizabeth Bonsignore, Sarah McRoberts, and Tamara Peyton. 2016b.  YouthTube: Youth Video Authorship on YouTube and Vine. In _Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing_ (2016-02-27) _(CSCW ’16)_. Association for Computing Machinery, New York, NY, USA, 1423–1437.  https://doi.org/10.1145/2818048.2819961
  * Young and Lo (2012) Charmian Ming Yan Young and Barbara Chuen Yee Lo. 2012.  Cognitive appraisal mediating relationship between social anxiety and internet communication in adolescents.  _Personality and Individual Differences_ 52, 1 (2012), 78–83.  https://doi.org/10.1016/j.paid.2011.09.001
  * Zhang et al. (2023) Yu Zhang, Changyang He, Huanchen Wang, and Zhicong Lu. 2023.  Understanding Communication Strategies and Viewer Engagement with Science Knowledge Videos on Bilibili. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_ (Hamburg, Germany) _(CHI ’23)_. Association for Computing Machinery, New York, NY, USA, Article 668, 18 pages.  https://doi.org/10.1145/3544548.3581476
  * Zywica and Danowski (2008) Jolene Zywica and James Danowski. 2008.  The Faces of Facebookers: Investigating Social Enhancement and Social Compensation Hypotheses; Predicting Facebook™ and Offline Popularity from Sociability and Self-Esteem, and Mapping the Meanings of Popularity with Semantic Networks.  _Journal of Computer-Mediated Communication_ 14, 1 (2008), 1–34.  https://doi.org/10.1111/j.1083-6101.2008.01429.x
  * 广东小天才科技有限公司 (2023) 广东小天才科技有限公司. 2023.  小天才电话手表官网.  https://www.okii.com.

##  Appendix A Interview Questions

###  A.1. Motivation and Overall Usage

####  A.1.1. Why buy?

  * •

Could you tell us a bit about your smartwatch? (Show and Tell)

  * •

What made you decide to get this smartwatch in the first place?

  * •

Are you still happy with your smartwatch?

  * •

Do you ever think about getting a new one? If so, why?

  * •

If you had the chance, would you consider buying a different brand or model?

####  A.1.2. Overall Usage

  * •

What do you usually do with your smartwatch? (Show and Tell)

####  A.1.3. Self-Presentation & Self-Disclosure within Smartwatch

  * •

Do you use the friend circle on your smartwatch?

  * •

Can you give us an idea of the kind of content you share? (Show and tell)

  * •

Do you pay attention to the number of likes and comments after posting?

  * •

Do you find smartwatch friend circles fun or boring?

  * •

Do you find your smartwatch friend circle convenient or inconvenient?

  * •

Do you use other platforms like QQ (Qzone), WeChat, Xiaohongshu, or Douyin
(TikTok)?

  * •

Do you post on these platforms as well?

  * •

How is the content you post on other platforms different from what you post on
your smartwatch?

  * •

Which platform do you prefer? Why?

###  A.2. Online Socialization through Smartwatches

####  A.2.1. Socialization Online

  * •

Can you share an experience of making friends with someone using a smartwatch?

  * •

Do you have any requirements for adding friends?

  * •

Can you share an experience of adding friends using the ”Bump-to-Connect”
feature?

  * •

What do you think is different about ”Bump-to-Connect” compared to other
methods?

  * •

After adding friends, what do you typically do together?

  * •

Do you have any expectations or requirements for your friends?

  * •

Have you ever unfriended someone or been unfriended by someone?

  * •

Did you meet these friends in real life or only through the smartwatch?

  * •

Do you have other ways to stay in touch with them?

  * •

Is there any difference between communicating with these friends on the
smartwatch and using QQ or WeChat?

  * •

Have you ever had any unpleasant experiences while communicating with these
friends on the smartwatch?

####  A.2.2. Online Communities & Group Dynamics within Smartwatches

  * •

Do you pay attention to any specific data on your smartwatches?

  * •

How do you feel about the likes or thumbs-up feature on smartwatches?

  * •

What do you typically do on your smartwatches?

  * •

Do you know “biaoquan”?

  * •

Do you know “jiazu”?

  * •

Besides that, what other social activities do people do on their smartwatches?

###  A.3. 3\. Offline Socialization around the Smartwatches

####  A.3.1. Socialization Offline

  * •

How many of your classmates or friends around you have smartwatches?

  * •

Do you and your classmates or friends talk about smartwatches?

  * •

Do you think smartwatches have affected your real-life friendships?

  * •

Have you ever had arguments or disagreements with someone because of
smartwatches?

###  A.4. Comparison and Speculative Design

####  A.4.1. Compare with Smartphones

  * •

What are the advantages of a smartwatch compared to a smartphone?

  * •

How does a smartwatch fare in terms of social interactions?

  * •

When it comes to protecting your privacy, which is more secure, a smartwatch
or a smartphone?

####  A.4.2. Speculative Design

  * •

If you could design your own smartwatch, what kind of features would you want
it to have?