Generative Human Motion Stylization in Latent Space
Authors: chuan guo
Yuxuan Mu
Xinxin Zuo
Peng Dai
Youliang Yan
Juwei Lu
Li Cheng
Venue: ICLR 2024 poster (2024)
Keywords: Human Motion Generation
Style Transfer
Generative Model
Primary area: representation learning for computer vision, audio, language, and other modalities
TL;DR: Our work propose a generative and versatile framework that is able to stylized an input motion non-determinstically using reference style motions, labels or even implicit learned priors.

Abstract: Human motion stylization aims to revise the style of an input motion while keeping its content unaltered. Unlike existing works that operate directly in pose space, we leverage the \textit{latent space} of pretrained autoencoders as a more expressive and robust representation for motion extraction and infusion. Building upon this, we present a novel \textit{generative} model that produces diverse stylization results of a single motion (latent) code. During training, a motion code is decomposed into two coding components: a deterministic content code, and a probabilistic style code adhering to a prior distribution; then a generator massages the random combination of content and style codes to reconstruct the corresponding motion codes. Our approach is versatile, allowing the learning of probabilistic style space from either style labeled or unlabeled motions, providing notable flexibility in stylization as well. In inference, users can opt to stylize a motion using style cues from a reference motion or a label. Even in the absence of explicit style input, our model facilitates novel re-stylization by sampling from the unconditional style prior distribution. Experimental results show that our proposed stylization models, despite their lightweight design, outperform the state-of-the-arts in style reeanactment, content preservation, and generalization across various applications and settings.

Published as a conference paper at ICLR 2024
GENERATIVE HUMAN MOTION STYLIZATION IN LATENT SPACE
Chuan Guo", Yuxuan Mul", Xinxin Zuo?, Juwei Lu², Li Cheng! UUiivessity of Alberta, Noah's Ark Lab, Huawei Canada
Peng Dai,,
Youliang Yan",
ABSTRACT
Human motion stylization aims to revise the style of an input motion while keep- ing its content unaltered. Unlike existing works that operate directly in pose space, we leverage the latent space of pretrained autoencoders as more expressive and robust representation for motion extraction and infusion. Building upon this, we present a novel generative model that produces diverse stylization results of sin- gle motion (latent) code. During training, a motion code is decomposed into two coding components: deterministic content code, and a probabilistic style code adhering to prior distribution; then a generator massages the random combina- tion of content and style codes to reconstruct the corresponding motion codes. Our approach is versatile, allowing the learning of probabilistic style space from either style labeled or unlabeled motions, providing notable flexibility in styliza- tion as well. In inference, users can opt to stylize a motion using style cues from a reference motion or a label. Even in the absence of explicit style input, our model facilitates novel re-stylization by sampling from the unconditional style prior dis- tribution. Experimental results show that our proposed stylization models, despite their lightweight design, outperform the state-of-the-arts in style reeanactment, content preservation, and generalization across various applications and settings.
INTRODUCTION
The motions of our humans are very expressive and contain a rich source of information. For ex- ample, by watching short duration of one individual's walking movement, we could quickly rec- ognize the person, or discern the mood, age, or occupation of the person. These distinguishable motion traits, usually thought of styles, are therefore essential in film or game industry for alistic character animation. It is unfortunately unrealistic to acquire real-world human motions of various styles solely by motion capture. Stylizing existing motions using reference style motion (i.e., motion-basede or preset style label (i.e., label-based) thus becomes feasible solution.
Deep learning models have recently enabled numerous data-driven methods for human motion styl- ization. These approaches, however, still find their shortfalls. A long line of existing works (Aber- man et al. 2020 Holden et al. 2016 Jang et al., 2022; Tao al. 2022) are limited deterministic stylization outcomes. (Park et al. 2021 Wen et al.) (021) though allows diverse stylization, their results are far from being satisfactory, and the trained models struggle generalize to other motion datasets. Furthermore, all of these approaches directly manipulate style within raw poses, redun- dant and potentially noisy representation of motions. Meanwhile, they often possess rigid designs, allowing for only supervised or unsupervised training, with style input typically limited to either reference motions or labels, as shown in Tab. [
In this work, we introduce novel generative stylization framework for 3D human motions. Inspired by the recent success of content synthesis in latent space (Guo et al. 2022a Chen et al.) 2022 Rom- bach et al.) [2022] Ramesh et al.) 2022), we propose use latent motion features (namely motion code) of pretrained convolutional autoencoders as the intermedia for motion style extraction and in- fusion. Compared to raw poses, the benefits are three-folds: (i) Motion codes are more compact and expressive, containing the most discriminative features of raw motions; (ii) Autoencoders can be
These authors contributed equally this work.

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Published as a conference paper at ICLR 2024
W(O.)
label: old
((O.D
Style inputs
Content inputs
Diverse results of lohei-hased (top) and prior based stylization (bottom)
(Top) Given input motion and target style label (i.e., old), label-based stylization generates diverse results following provided label. (Bottom) Without any style indicators, our prior-base based method ran- domly re-stylizes the input motion using sampled prior styles 2.. Five distinct stylized motions from the same content are presented, with poses synchronized and history in gray. See Fig.3 and (d) for implementations.
Figure 1:
learned once on large dataset and reused for downstream datasets. Thanks to the inductive bias of CNN (LeCun et al. 1995), the learned motion code features typically contains less noise, resulting in improved generalization. code sequences are much shorter than motions, making them more manageable in neural networks. Building this, our latent stylization framework decomposes the motion code into two compo- nents: a temporal and deterministic content code, and a global probabilistic style code confined by prior Gaussian distribution. The subsequent generator recombines content and style to synthesize valid motion code. During training, besides auto-encoding and decoding, we swap the contents and styles between random pairs, and the resulting motion codes are enforced recover the source con- tents and styles through cycle reconstruction. To further improve content-style disentanglement, we propose a technique called homo-style alignment, which encourages the alignment of style spaces formed by different motion sub-clips from the same sequence. Lastly, the global velocity of resulting motions are obtained through a pre-traince global motion regressor.
empirically demonstrated in Tables IT] (iii) Practically, motion
Our approach offers versatile stylization capabilities (Tab. []. accommodating various conditioning options during both training and inference: 1) Deterministic stylization using style from exemplar motions; 2) In the label conditioned setting, model can perform diverse stylization based on provided style labels, as in Fig. D (top); 3) In the unconditional setting, our model can randomly sample styles from the prior distribution to achieve stochastic stylization, in Fig. (bottom). Benefiting from our latent stylization and lightweight model design, our approach achieves state- of-the-art performance while being 14 times faster than the most advanced prior work (Jang al., 2022). as shown in Table 5
Our key contributions can be summarized follows. Firstly, we propose novel generative frame- work, using motion latent features as an advanced alternative representation, accommodating vari- ous training and inference schemes in single framework. Secondly, through a comprehensive suite of evaluations on three benchmarks, our framework demonstrates robust and superior performance across all training and inference settings, with notable efficiency gains.
RELATED WORK
Image style in computer vision and graphics is typically formulated as the global statistic features of images. Early work (Gatys et al. 2016) finds it possible to transfer the visual style from one image to another through aligning their Gram matrices in neural networks. On top of this, (Johnson et al. 2016; Ulyanov et al. 2016a) enable faster transferring through an additional feed-forward neural networks. The work (Ulyanov et al.) (016bb realizes that instance normalization (IN) layer could lead to better performance. However, these works can only be ap- plied on single style image. (Huang & Belongie 2017) facilitates arbitrary image style transfer by introducing adaptive instance normalization (AdalN), Alternatively, in PatchGAN (Isola et al.) 2017)) and CycleGAN (Zhu et al. (207)), textures and styles are translated between images by ensur- ing the local similarity using patch discriminator. Similar idea was adopted in (Park et al. 2020
Image Style Transfer.
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Published as a conference paper at ICLR 2024
Supervised (ir style lahel) Label-busee
Unsupervised (wol style label) Moboo--bsse
Generative
Motion bussen
Prtor based
Xin ml
2015
Holden 2016
2017
Aberman of 2021
Iao 0i
2022
aan
Ours
Table I: Our generative framework owns flexible design for training and inference.
which proposes patch co-occurrence discriminator that hypothesizes images with similar marginal and joint feature statistics appear perceptually similar.
Motion Style Transfer. Motion style transfer has been a long-standing challenge in computer animation. Early work (Xia al. 2015) design online style transfer system based KNN search. (Holden et al. 2016 Du et al. 2019; Yumer & Mitra (016)) transfers the style from refer- ence source motion through optimizing style statistic features, such Gram matrix, which computationally intensive. Feed-forward based approaches (Holden et al.) 2017 Aberman et al., 2020 Smith et al.) 2019) properly address this problem, where (Aberman al.) (2020) finalizes a wwo-branch pipeline based on deterministic autoencoders and AdalN (Huang & Belongie, 2017) for style-content disentanglement and composition; while (Smith al. (219)) manages to stylize existing motions using one-hot style label, and models it as class conditioned generation process. More recently, with the explosion of deep learning techniques, some works adopt graph neural net- works (GNN) (Park et al. 2021 Jang et al. 2022). advanced time-series model (Tao et al. 2022 Wen et al., 2021). or diffusion model (Raab et al. 2023) to the motion style transfer task. Specifi-
cally, (aag et al. 2022 realizes framework that extracts style features from motion body parts.
Synthesis in Latent.
Deep latent have been evidenced promising alternative representation
for content synthesis including images (Rombach et al. 2022 Ramesh et al., 2022] Esser et al,
2021b at Ding et al.
202I). motion (Guo et al.) 2022a b Gong al.) 2023 Chen al. 2022
3D shape (Zeng et al.
2202; Fu et al.] 2022). and video (Yan et al. [0l1]]221]]] Hu et al.
2023 3. These
works commonly adopt two-stage synthesis strategy. At the first stage, the
contents encoded into continuous latent maps (e.g., using autoencoders, CL.IP (Radford et al.) (022)) )) or
discrete latent tokens through VQ-VAE (Van Den Oord et al. 2017 Then, models are learned to generate these latent representation explicitly which can be inverted to data space in the end. This strategy has shown several merits. Deep latent consists of the most representative features of raw data, which leads to more expressive and compact representation. It also cuts down the cost of time and computation during training and inference. These prior arts inspire the proposed latent stylization in our approach.
GENERATIVE MOTION STYLIZATION
An overview of our method described in Figure] Motions are first projected into the latent space (Sec. With this, the latent stylization framework learns to extract the content and style from the input code (Sec. 3.2). which further support multiple applications during inference (Sec. 3.3
3.1 MOTION LATENT REPRESENTATION
As pre-processing step, we learn motion autoencoder that builds the mapping between motion and latent space. More precisely, given a pose sequence P TxD, where T denotes the number of poses and D pose dimension, the encoder & encodes P into motion code z {PP) E IDD, with T, and D= the temporal length and spatial dimension respectively, and then decoder D recovers the input motion from the latent features, formally P = D(z) = D(E(P)).
A well-learned latent space should exhibit smoothness and low variance. In this work, experi- ment with two kinds of regularization methods in latent space: 1) as in VAE (Kingma & Welling 2013), the latent space is formed under light KL regularization towards standard normal distri- bution Cid = (z)lVV 1))= ; and 2) similar to (Guo et al. 2022a), we train the classical autoencoder and impose L.1 penalty on the magnitude and smoothness of motion code sequences,
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2d
AutoEncode (Homo-style)
Swap
Cycle reconstruction
Motion Space
D
z²
Style
K
IN
Zg S
AdalN
space
Content oode
(a) Architecture
(b) Learning Schemne
Figure 2: Approach overview, (a) A pre-trained autoencoder & and D (Sec. 3.1) builds the mappings between motion and latent spaces. Motion (latent) code further encoded into two parts: content code from content encoder (E.),
style space N, from style encoder (E,,) that take style label as an additional input. The content code (z.) is decoded back to motion code (2) via generator G. Meanwhile, style code z. is sampled from style space (N.), together with style label (sl), which are subsequently injected to generator layers through adaptive instance normalization (AdaIN). (b) Learning scheme, where style label (sl) is omitted for simplicity. Our model trained by autoencoding for content and style coming from the same input. When decoding with content from different input (i.e, swap), we enforce resulting motion code (ž') to follow cycle reconstruction constraint. For motion codes (z', z²) segmented from the same sequence (homo-style),, their style spaces assumed to be close and learned with style alignment loss Chsa.
giving S. = Au ||| + Asms Zi: Z Our motion encoder & and decoder D are simply 1-D convolution layers with downsampling and upsampling scale of 4 (i.e., T 4T2), resulting in more compact form of data that captures temporal semantic information.
3.2 MOTION LATENT STYLIZATION FRAMEWORK
As depicted in Figure 2 our latent stylization framework aims to yield valid parametric style space, and meanwhile, preserve semantic information content codes much as possible. This is achieved by our specific model design and dedicated learning strategies.
3.2.1 MODEL ARCHITECTURE.
There are three principal components in our framework: style encoder E,, content encoder Eg and generator G, in (a)
Probabilistic Style Space. For style, existing works (Park et al. 2021 Aberman et al., 2020 Jang cct al. (022) generate deterministic style code from motion input. In contrast, our style encoder E., taking z and style label sf input, produces a vector Gaussian distribution NC, (pa, o.) to formulate the style space, from which style code R P is sampled. In test-time, this probabilistic style space enables us to generate diverse and novel style samples.
Comparing to style features, content features exhibit more locality and determinism. Therefore, model them deterministically to preserve the precise structure and meaning of the motion sequence. The content encoder converts the motion code RT.xD. into content code z, x that keeps temporal dimension Th where global statistic features (style) are erased through instance normalization (IN). The asymmetric shape of content code Z, and style code z, are designed of purpose. We expect the former to capture local semantics while the latter encodes global features, what style is commonly thought of. Content code is subsequently fed into the convolution-basee generator G, where the mean and variance of each layer output are modified by an affine transfor- mation of style information (i.e., style code and label), known as adaptive instance normalization (AdaIN). The generator aims to transform valid combinations of content and style into meaningful motion codes in the latent space.
3.2.2 LEARNING SCHEME
With the model mentioned above, propose series of strategies for learning disentangled content and style representations. F illustrates our learning scheme. Note the input of style label sl is omitted for simplicity. During training, for each iteration, design three groups of inputs: z!,
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Content code
M
Contern code
-0-00
Supervised
Style
Style
Skyle
Style
Style motion
(a) Motion based stylization
(b) Label- basedasedddd stylization
Content code
SAM
Content code
Unsupervised
Style space
Style Code
(c) Motion-baseee stylization
(d) Prior-based stylization
Figure 3: During inference, our approach can stylize input content motions with the style cues from (a, c) motion, (b) style lahel and (d) unconditional style prior space.
z² and z², where z! and z² are motion code segments coming from the same sequence and z can be any other segments.
AutoEncoding Cree- We train our latent stylization framework partly through autoencoding, that given motion latent codes, like x² and z² the generator learns to reconstruct the input from the corresponding encoded content and style features, formally 2 (z),(z), (z)). For accurate reconstruction, we decode the resulting motion latent codes (2² and 2²) back to motion space (P1
and P22 through D, and apply Li-distance reconstruction in both latent and motion space:
Cree
Σ 12 2'1l1 (E(1.2)
|| - P
(I)
For the motion segments in one motion sequence, we could usually assume their styles similar in all aspects. This is strong supervision signal especially when style annotation is unavailable, dubbed homo-style alignment in our work. Since x! and x² belong to the same sequence, their learned style spaces are enforced to be close:
Homo-style Alignment Chee.
Lhea DKL (KL ( (12)
Swap and Cycle Reconstruction Lewe- To further encourage content-style disentanglement, adopt a cycle consistency constraint (Zhu et al.) 2017 Jang et al. 2022) when content and style are swapped between different motion codes, such as z and 2 in Fig. 2 Specifically, the generator takes as input the content from z² and the style from z², and then produces a new transferred motion code z', which supposed to preserve the content information from z² and the style from z Therefore, if we re-combine z'ss content and z²'s style, the generator should be able to restore z.2 The same to 2 that are recovered from the mix of z" style and z content
Laye Σ 1 -
| PP'l
(E(2.3) To ensure smooth and samplable style spaces, we apply a KL loss regularization all style spaces:
Lu Σ
(pt,
(E(1.2,3,1)
Overall, our final objective is L Lree+ +hraa Ahsahhsa Aeye AEICRI We also have experimented adversarial loss for autoencoding and cycle reconstruction in (Park et al. [021] Aberman al.,
2020
Tao et al. 2022), which however appears to be extremely unstable in training.
Unsupervised Scheme (w/o Style Label). Collecting style labeled motions is resource- consuming. Our approach can simply fit in the unsupervised setting with just one-line change of code during training-to drop out style label sl input.
Difference of N, Learned w and w/o Style Label. While learning with style label, since both the style encoder E, and generator are conditioned on style label, the style space is encouraged to leam style variables other than style label as illustrated in Fig. 8 (d). Whereas in the unsupervised setting where the networks are agnostic to style label, in order to precisely reconstruct motions, the style space is expected to cover the holistic style information, including style label (see Fig. 8 (c)).
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3.2.3 GLOBAL MOTION PREDICTION
Global motion (i.e., root velocity) is perceptually a more sensitive element than local joint motion (e.g., foot skating). However, given one motion, transferring its global motion to another style domain is challenging without supervision of paired data. Previous works commonly calculate the target global motion directly from the content motion, or enforce them to be close in training. This may fail when the transferred motion differs a lot from the source content. In our work, we propose simple yet effective alternative, which is small ID convolutional network that predicts the global motion from local joint motion, simply trained on unlabeled data using objective of mean absolute error. During inference, the global motion of output can be accurately inferred from its local motion.
3.3 INFERENCE PHASE
As displayed in Figure 3. our approach at run time can be used in multiple ways. In supervised setting: a) motion-based stylization requires the user to provide style motion and a style label as the style references; and b) label-based stylization only asks for a target style label for stylization. With sampled style codes from standard normal distribution AN I). we are able to stylize source content motion non-deterministicllly In the case of unsupervised setting: c) motion-based styliza- tion, which similarly, yields a style code from a reference motion; and d) prior-based stylization that samples random style codes from the prior distribution (O, I). Since there is no other pretext style indications, the output motion could carry any style trait in the style space.
4 EXPERIMENTS
We adopt three datasets for comprehensive evaluation. (Aberman et al. 2020) is a widely used motion style dataset, which contains 16 distinct style labels including angry, happy, Old, etc, with total duration of 193 minute. (Xia et al.] (015) is much smaller motion style collection (25 mins) that captured in 8 styles, with accurate action type annotation (8 actions). The motions are typically shorter than 3s. The other one is CMU Mocap (CMU). an unlabeled dataset with high diversity and quantity of motion data, All motion data retargeted to the same 21-joint skeleton structure, with a 10% held-out subset for evaluation. Our autoencoders and global motion regressor are trained on the union of all training sets, while the latent stylization models are trained excursively on (Aberman) et al., 2020), using the other two for zero-shot evaluation. During evaluation, from (Aberman et al. (2200) test sets to stylize the motions from one of the three test sets. Style space learned based motions of 160 poses (5.3s). Note our models supports stylization of arbitrary- length content motions. See Appendix D for implementation details and full model architectures.
use the styles
Metrics in previous motion stylization works heavily rely on a sparse set of measurements, typi- cally human evaluation and style accuracy. Here, design suite of metrics comprehensively evaluate our approach. We firstly pre-train a style classifier on (Aberman et al. 2020) train set, and use it as a style feature extractor to compute style recognition accuracy and style FID. For dataset with available action annotation ( (Xia et al. 2015)), an action classifier is learned to extract content features and calculate content recognition accuracy and content FID. We further evaluate the con- tent preservation using geodesic distance of the local joint rotations between input content motion and generated motion. Diversity in (Lee et al., 2019) is also employed to quantify the stochasticity in the stylization results. Further explanations are provided in Appendix E
Baselines. We compare our method to three ofate-of-the-art methods (Aberman et al., 2020 Jang
jt al., 2022 Park al., (02T) in their respective settings. Among these, (Aberman al. and (Park et al. (021) are supervised methods learned within GAN framework. (Park et al. learns par-label style space, and a mapping between Gaussian space and style space. At run time, supports both deterministic motion-based and diverse label-based motion stylization.
2020)
2021
4.1
QUANTITATIVE RESULTS
Table and Table present the quantitative evaluation results the test sets of (Aberman al. 2020).
CMU Mocap (CMU) and (Xia et al.] (013).. Note the latter two datasets are completely
unseen to our latent stylization models. We generate results using motions in these three test sets

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Published as a conference paper at ICLR 2024
Setting
Methods
Sisle Accunaes
Aberaun
2020
DIVT
Accurncy
CMU
FID.
CMU
DRL
Dir
Real Mutions
0,007-1922
0.002 200
0.50
D.007
Abreman
0. 0.378-9t6
0.485-000
1100K 1902
0,89149u:
0.305
0.501
0.674
D.19g 4113
D.G M4
Mosion based (5)
0.5022005
0.022
0
0.579
OurstV)
0u0gg: 4007
0.00y
Ours (A)
0.945
0.844
mre
Park
0.013 4901
0.57 1000
000
0,511-4000
0.22y4400 A00
Label-haood
Ozs
Ours (V)
0.81g
0,029
0.023
0. 131
B 2.8223--00
0.50gmaa
0. 0.027
Bu0Gg
00014 5402
2.611 M
0
Ours (A]
0,942 4903
0.012
0.344
0,000
0,512
0.571
20067
Jang ct 2002
0,533
0.047
0. 0.50
0.703g
BUGG
0. 0.725
Motion bused (U)
Oujs win becae
OarstV) (V)
0. 0.78p2814
0.006 1933
0.015
D. B.A
0.475
0.822
0.26) 0.261
0. 0.052
0. 4.4455
9U672-4633
Ours LA)
0 0.80g
0,000
0. 0.441
0.770
Ouns wel lasent
D.
1. 11162
1 1,25-400
Price har--aseei()) (U)
Ours (V)
0.418
1.00genra
D.611
0.667
Curs (A)
0.136 200
1.187
0.841 2000
0.
Table 2: Quantitative results on the (Aherman et al. 2020) and CMU Mocap test sets, + indicates 95%
confidence interval. Bold face indicates the hest result, while underseore refers to the second best. (S) and (U) denote supervised and unsupervised setting, (V) VAE and (A) AE represent different latent models in Sec. 3.1
Sctting
Mitholk
Acc
Concer Aoe
eta
2015
FID.
Setting
Method
Ouas wins
Mpemon al
0.31-403
0.80p-023
Aberman et al 2020
78.69%
york
0.42
M-hased (8)
Park
73.67%
M--ased
0.2075522
65. 65.00%
Ous
(A)
0.934
0.020 20
0.074
0
0.180 4ex
0.20 49
6.716+422
L-baned
2021
73.06%
cral 2001
0:7704433
17yd0te
M-hosed (U)
Jang cl.
2002
58.9255
Baggeers
L-boded (8)
0.96g-i9
0.705-423
4.228
pang 2311
2.00--40 4414
0.103 1.05
082
D1M6G /013
Table 4: Human evaluation results.
M-hsoal (1)
2002
0.58gte1
0.221 ***
0.747
0
721 ttes
Aberman 2000
Methods
Runtime [mo)]
0. 0.688 +eu
0.217
0,775 ex
Pink Aora
32.247
Utrs Issent
0.246 1097
0.70x 1G33
1,183 1909
10% e
16.329
Plaaed
Oun-
0. 4853
0.20y 1.00
0 P04 1082
0 0887--00
mig c
67.565
Oun
0.5gg 48us
0.227 6000
0.708 442
C (R)
4.760
Table 3: Quantitative results on the (Xia et al. 2015) test set.
Table 5: Runtime comparisons.
as content, and randomly sample style motions and labels from (Aberman et al. 2020) test set. For fair comparison, we repeat this experiment 30 times, and report the mean value with a 95% confidence interval. We also consider the variants of our approach: non-latent stylization (ours w/o luten(), using VAB (Ours (V)) or AE ( Ours (A)) as the latent model (See Sec. [3.1. Ours w/o latent employs the identical architecture as our full model, as illustrated in Fig. 2 (a), without the steps of pretraining or training the motion encoder & and decoder D as autoencoders. Although it maintains the same number of model parameters, directly learns style transfer on poses, allowing us to assess the impact of our proposed latent stylization.
Overall, our proposed approach consistently achieves appealing performance on a variety of appli- cations across three datasets. In the supervised setting. GAN approaches, such as (Aberman et al.) 2020) and (Park et al., 202T), tend to overfit on one dataset and find it difficult to scale to other motions. For example, (Park et al.) (02T) earns the highest achievement on style recognition on (Aberman et al. 2020). as 97.1%, while underperforms on the other two unseen datasets, with style accuracy of 81.3% and 79.6%. Furthermore, these methods usually fall short in preserving con- tent, as evidenced by the low content accuracy (31.8% and 44.1%) in Tab, 3 (Jang et al.] 2022) is shown to be strong unsupervised baseline; it gains comparable and robust performance on differ- ent datasets, which though still suffers from content preservation. On the contrary, our supervised and unsupervised models commonly maintain high style accuracy over 90% and 80% respectively, with minimal loss on content semantics. Among all variants, latent stylization improves the perfor- mance on almost all aspects. including generalization ability, with slight compromise on diversity. Ours (V) tends to own higher success rate of style transfer, while ours (A) typically outperforms on maintaining content (i.e., Geo Dis and Content Accuracy).
User Study. In addition, user study on Amazon Mechanical Turk is conducted to perceptually evaluate our motion stylization results. 50 comparison pairs (on CMU Mocap (CMU)) between each baseline model and approach, in the corresponding setting, are generated and shown to 4 users, who are asked to choose their favored one regarding realism and stylization quality. Overall, collect 992 responses from 27 AMT users who have master recognition. As shown in Table
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AMM
Soola Cop
Style Content
[c] Park et al. [2021]
(a) Ours (supervised)
(b) Ours (innuprriioe)
(d) lang et al. [2022]
Figure 4: Qualitative comparisons of motion-based stylization. Given the style mótion (green) and content motion (blue), we apply stylization using our methods (orange). (Park et al. 2021) (supervised), and (Jang
et al. (022) (unsupervised). The content motions top two cases come from (Aberman et al.) (020). while the bottom two from CMU Mocap (CMU) test sets. Example artifacts are highlighted using red signs.
our method earns more user appreciation over most of the baselines by a large margin. Further user study details are provided in Appendix F
Efficiency. Table presents the comparisons average time cost for a single forward pass with 160-frame motion inputs, evaluated single Tesla P100 16G GPU. Previous methods apply style injection at each generator layer until the motion output and usually involve computationally inten- sive operations such multi-scale skeleton-bai based GCN and forward-looy kinematics. Benefiting from our latent stylization and lightweight network design, our model appears to be much faster and shows the potential for real-time applications.
4.2 QUALITATIVE RESULTS
Figure presents the visual comparison results on test sets (Aberman et al., 2020) (top two)
and CMU Mocap (CMU) (bottom two), in supervised (ours vs. (Park et al. 2021)) and unsupervised (ours vs. (Jang et al. (022 settings. For our model, we use ours( by default. In the unsupervised transferring style from style motion to content motion; but it sometimes changes the actions from content motion, as indicated red circles. Supervised baseline (Park et al.) [2021) follows similar trend. Moreover, the results of (Park al.) 2021) on CMU Mocap (Aberman et al. (2020) commonly fail to capture the style information from input style motion. This also agrees with the observation of the limited generalization ability of GAN-based models in Tab. 2 Other artifacts such as unnatural poses ( (Aberman et al. 2020 Park al. 202Th) and foot sliding( (Jang et al., 2022 can be better viewed in the supplementary video. This can be partially attributed to the baselines directly applying global velocities of content motion for stylization results. In contrast, our approach shows reliable performance in both maintaining content semantics and capturing style characteristics for robust stylization.
setting, (Jang et al. 2022 has comparable performance
Diverse and Stochastic Stylization. Our approach allows for diverse label-based and stochastic prior-based stylization. As presented in Figure 5 for label-based stylization, taken content
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TIFTT TITTT TAATK TYMM MWW IEVU
NIMA
ROMA
MAMS
Content motions
Diverse stylications from style (top), [bottom]
Diverse styizations of samp ed styl priors
Figure 5: Two examples of diverse label-based stylization (middle) and prior-based stylization (right).
Promt:: person sallks backward in small iig [/emnlemmdel style]
steps sideways. [Drunk stylel
Conterk
Syye
Stulized T2M
Stylized T2M
Figure 6: Two stylized tex12motion examples, by applying method behind text2motion Guo ct al. 2022a
motion and style label as input, our model is able to generate multiple stylized results with inner- class variations. i.e., different manners of old man walking. On the other hand, our prior-based stylization can produce results with very distinct styles that are learned unsupervisedly from motion data. These styles are undefined, and possibly non-existent in training data.
Stylized Text2Motion. Text-to-motion synthesis has attracted significant interests in recent years (Guo al. 2022a Petrovich et al. 2022 Tevet et al. 2022); however, the results often ex- hibit limited style expression. Here in Fig. 6 we demonstrate the feasibility of generating stylistic human motions from the text prompt, by simply plugging our model behind text2motion genera- tor (Guo et al. 2022a). It is worth noting that the motions from (Guo et al. (002a)) differ greatly from our learning data terms of motion domain and frame rate (20 fps vs. ours 30 fps).
Style Code Visualization. Figure & displays the (-SNE 2D projection of our extracted style codes using four model variants, where each sample color-coded according to its label. In the unsupervised setting, each style code is as- sociated with global style features that are ex- pected to be distinctive with respect to the style category. It can observed that our latent styl- ization method produces clearer style clusters aligned with style labels compared to our non- latent method, with VAE-based latent model (ours(V) V)) performing the best. While in the supervised setting, discussed in Sec. 3.2.22 our approach learns label-invariant style fea-
Hear
(a) Ours w/oo latent
[5] Ours [A] (Umppeniiu))
Hamacc
Jimppensie))
Ours [uisuperviee)
Ours [V]
[superissof]
Figure 7: Style code visualization.
tures (Fig ( (d)): These style features may arise from individual and environmental factors.
CONCLUSION
Our work looks into the problem of 3D human motion stylization, with particular emphasis on generative stylization in the neural latent space. Our approach learns probabilistic style space from motion latent codes; this space allows style sampling for stylization conditioned on reference style motion, target style label, free-form novel re-stylization. Experiments three mocap datasets also demonstrate other merits of our model such as better generalization ability, flexibility in style controls, stylization diversity and efficiency in the forward pass.

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Ethics Statement. In practice use, method is likely cause demographic discrimination, it involves stereotypical styles related to gender (Femalemodel), age (old) and occupation (soldier).
Reproducibility Statement. We have made best efforts ensure reproducibility, including but not limited to: 1) detailed description of our implementation details in Appendix D 2) detailed description of our baseline implementation in Appendix [D3] 3) graphic illustration of our model architecture in Figures and I0 and 4) uploaded codes as supplementary files.
REFERENCES
Kfir Aberman, Yijia Weng, Dani Lischinski, Daniel Cohen-Or, and Baoquan Chen. Unpaired motion style transfer from video to animation. ACM Transactions on Graphics (TOG). 39 2020.
Xin Chen, Biao Jiang, Wen Liu, Zilong Huang. Bin Fu, Tao Chen, Jingyi Yu, and Gang Yu. Ex- ecuting your commands via motion diffusion in latent space. arXiv preprint 1.Xi::2212.04048, 2022.
CMU. Carnegie- mocap database. Retrieved from
Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, et al. Cogview: Mastering text-to-image generation via transformers. Advances in Neural Information Processing Systems, 34: 19822- 2021.
Han Du, Erik Herrmann, Janis Sprenger. Klaus Fischer, and Philipp Slusallek. Stylistic locomotion modeling and synthesis using variational generative models. In Proceedings of the 12th ACM SIGGRAPH Conference on Motion, Interaction and Games, pp. 1-10, 2019.
Patrick Esser, Robin Rombach, Andreas Blattmann, and Bjorn Ommer. Imagebart: Bidirectional context with multinomial diffusion for autoregressive image synthesis. Advances in Neural Infor- mation Processing Systems, 34:3518-333 -3318, 2021a.
Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming transformers for high-resolution image synthesis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recogni- tion, pp. 12873-12883, 2021b.
Rao Fu, Xiao Zhan, Yiwen Chen, Daniel Ritchie, and Srinath Sridhar. Shapecrafter: A recursive text-conditioned 3d shape generation model. arXiv preprint (xXiv:2207.09444: 2022.
Lcon A Gatys, Alexander S Ecker, and Matthias Bethge. Image style transfer using convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recog- nition, pp. 2414-2423, 2016.
Kehong Gong, Dongze Lian, Heng Chang, Chuan Guo, Xinxin Zuo, Zihang Jiang, and Xinchao Wang. Tm2d: Bimodality driven 3d dance generation via music-text integration. arXiv preprini arXiv:2304.02419, 2023.
Chuan Guo, Shihao Zou, Xinxin Zuo, Sen Wang, Wei Ji, Xingyu Li, and Li Cheng. Generating diverse and natural 3d human motions from text. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5152-5161, 2022a.
Chuan Guo, Xinxin Zuo, Sen Wang, and Li Cheng, Tm2t: Stochastic and tokenized modeling for the reciprocal generation of 3d human motions and texts. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXXV, pp- 580-597. Springer, 2022b.
Daniel Holden, Jun Saito, and Taku Komura. A deep learning framework for character motion synthesis and editing, ACM Transactions on Graphics (TOG), 35(4):1-11, 2016.
Daniel Holden, Ikhsanul Habibie, Ikuo Kusajima, and Taku Komura. Fast neural style transfer for motion data. JEEE computer graphics and applications, 37(4)422-49 2017.
Yaosi Hu, Zhenzhong Chen, and Chong Luo. Lamd: Latent motion diffusion for video generation. arXiv preprint I 1603, 2023.
10

---

Published as a conference paper at ICLR 2024
Xun Huang and Serge Belongie. Arbitrary style transfer in real-time with adaptive instance normal- ization. In Proceedings of the IEEE international conference on computer vision, pp. 1501-1510, 2017.
Phillip Isola, Jun- Yan Zhu, Tinghui Zhou, and Alexei A Efros. Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and paltern recognition, pp. 1125-1134, 2017.
Deok-Kyeooo Jang, Soomin Park, and Sung-Hee Lee. Motion puzzle: Arbitrary motion style trans- fer by body part. ACM Transactions on Graphics (TOG), 41(3):1-16, 2022.
Justin Johnson, Alexandre Alahi, and Li Fei-Fei. Perceptual losses for real-time style transfer and super-resolution. In Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part 14, pp. 694-711. Springer, 2016.
Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative the IEEE/CVF conference on computer vision and pattern
adversarial networks. In Proceedings the recognition, pp. 4401-4410, 2019.
Diederik P Kingma and Max Welling. 1rXiv:1312.66. 14, 2013.
Auto-encoding variational bayes. arXiv preprint
Yann LeCun, Yoshua Bengio, et al. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361 1995.
Hsin-Ying Ying Lee, Xiaodong Yang, Ming- Yu Liu, Ting-Chun Wang, Yu-Ding Lu, Ming- -iug-Hsuann Yang, and Jan Kautz. Dancing to music. Advances in neural information processing systems, 32, 2019.
Soomin Park, Deok-Kyeong Jang, and Sung-Hee Lee. Diverse motion stylization for multiple style graph-based generative model. Proceedings of the ACM on Com-
domains via puter Graphics and Interactive Techniques, 4(3):1-17, 2021.
Taesung Park, Jun-Yan Zhu, Oliver Wang, Jingwan Lu, Eli Shechtman, Alexei Efros, and Richard Zhang. Swapping autoencoder for deep image manipulation. Advances in Neural Information Processing Systems, 33:7198-7211, 2020.
Mathis Petrovich, Michael J Black, and Gül Varol. Temos: Generating diverse human motions from textual descriptions. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXII, pp. 480-497. Springer, 2022.
Sigal Raab, Inbal Leibovitch, Guy Tevet, Moab Arar, Amit H Bermano, and Daniel Cohen-Or. Single motion diffusion. arXiv preprint arXiv: 2023.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference machine learning, pp- 8748-8763. PMLR, 2021.
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text- conditional image generation with clip latents. arXiv preprint arXiv:2204.06125. 2022.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjöm Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pp. 10684-10695, 2022.
Harrison Jesse Smith, Chen Cao. Michael Neff, and Yingying Wang. Efficient Neural Networks for Real-time Motion Style Transfer. Proceedings of the ACM Computer Graphics and Interactive Techniques, 2(2): 1-7,, July 2019. ISSN 2577-6193. doi: 10. 10.1145/3340254. URL https:
//dl. acm www.orgg///oooo.. /10 1145/3340254
Tianxin Tao, Xiaohang Zhan, Zhongquan Chen, and Michiel van de Panne. Style-ERD: Respon- sive and Coherent Online Motion Style Transfer. In 2022 JEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR), pp. 6583 3583-6593,, New Orleans, LA, USA. June doi: 10.11099CVVR55268.2222222. 200.5222688.2220064556 URL http::
2022. IEEE ISBN 978- 678-1-665446946633
7/1 7/ieeexplore. aee. wwt.///wwww.o....oeeeeeeeeeeee/e

---

Published as a conference paper at ICLR 2024
Guy Tevet, Brian Gordon, Amir Hertz, Amit H Bermano, and Daniel Cohen-Or. Motionclip: Ex- posing human motion generation to clip space. Computer Vision-ECCV 2022: 17th Euro- pean Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXII, pp. 358-374. Springer, 2022.
Dmitry Ulyanov, Vadim Lebedev, Andrea Vedaldi, and Victor Lempitsky. Texture networks: Feed- forward synthesis of textures and stylized images. arXiv preprint arXiv:1603.03417, 2016a.
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. Instance normalization: The missing in- gredient for fast stylization. arXiv preprint arXiv:1607.08022 2016b.
Aaron Van Den Oord, Oriol Vinyals, al. Neural discrete representation learning. Advances in neural information processing systems, 30, 2017.
Yu-Hui Wen, Zhipeng Yang, Hongbo Fu, Lin Gao, Yanan Sun, and Yong-Jin Liu. Autoregressive stylized motion synthesis with generative flow. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13612-13621, 2021.
Shihong Xia, Congyi Wang, Jinxiang Chai, and Jessica Hodgins. Realtime style transfer for unla- beled heterogeneous human motion. ACM Transactions on Graphics (TOG), 34(4):1-10, 2015.
Wilson Yan, Yunzhi Zhang, Pieter Abbeel, and Aravind Srinivas. Videogpt: Video generation using vq-vae and transformers. arXiv preprint arXiv:2104.10133 2021.
M. Ersin Yumer and Niloy Mitra. Spectral style transfer for human motion between independent actions. ACM Transactions on Graphics, 35(4):1-8, July 2016. ISSN 0730-0301, 1557-7368. doi: 10.1145/2897824.2225555 URL hRtpss//:/lll acm. wttp.//..o../o///011111////11110
2925955
Xiaohui Zeng, Arash Vahdat, Francis Williams, Zan Gojeic, Or Litany, Sanja Fidler, and Karsten Kreis. Lion: Latent point diffusion models for 3d shape generation. Advances in Neural Information Processing Systems (NeurIPS), 2022.
Yi Zhou, Connelly Barnes, Jingwan Lu, Jimei Yang, and Hao Li. On the continuity of rotation representations in neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pp. 5745-5753, 2019.
Jun- Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. Unpaired image-to-iman translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision, pp. 2223-2232, 2017.
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This supplementary file provides additional ablation analysis of design components (Ap- pendix A and weight of various loss terms (Appendix C. content and feature visualiza- tion(Appendix [B, implementation details (Appendix D). Evaluation Metric (Appendix E). cross- style and homo-style interpolation results (Appendix G). user study introduction (Appendix and failure cases (Appendix H)).
Video. We also provide several supplementary videos, which contains dynamic animations of our stylization results, visual comparisons, interpolation, stylized text2motion and failure cases. We strongly encourage our audience to watch these videos. It will be much helpful to understand our work. The videos are submitted along with supplementary files, or accessible online (1080P): https://driveggooleecco
.dBiddBBB "uspsshnupp
Code and Model. The code of our approach and implemented baselines are also submitted for reference. Code and trained model will be publicly available upon acceptance.
A ABLATION ANALYSIS
Aberman et al. 2020
Xin et al.
2015
S/U
Method
Style Acci
Style
Geo Dis
Style Acct
Content AccT
Content FID
Geo Dis J
S
Ours (A)
0.945 2007
:.020
0.344
0,926=000
0.674#001
0.189
w/o latent
0,932=008
0.022#002
0.463#003
0,851=012
0.65
0.258
0.707
w/ prob-style
0.913
0,022
0.509
0,870=010
0.521 Ho1s
0.219
0.767
weo homo-style
0.883
0.032200s
0.507
0.851 0,851=012
0.537 d.13
0.232
0.760 0.760
w/o autoencoding
0.900
0.026=002
0.127
0.879=010
0. #o11
0.198
0.720
w/o cyclo-recon
0.917=000
0.021 2002
0.385
0.872
0. 6.72710111
0. 0.208 8:004
0.69
Ours (A)
0.804 1:011
0.040
0.441 +003
0.814 4001
0.58
0.217-006
0.733
who latent
0,780 -014
0,048
0.46640004
2014
0.581
0.2 0:272:1:00
0.721 10000
w/o prob-style
0.734
0.0584008
0.461 4:0x
0.666-016
1.597
0.270
0.718
vho homo style
0.763-016
0.050=002
0.51 3-513
0. 030000000
0. 0.52640ra
0.250
0.803
w/o autoencoding
0.77
0.0492000
0.493
0.811 2011
0. 191 do1s
0. t.230
who cycle-recon 0.765 2011
0.04g4000
0.560
0.756-91 -017
0.4794913
0.233 4-00
0.86
Table 6: Ablation study on different components of our model design. + indicates 95% confidence interval. Bold face indicates the best result, while underscore refers to the second best. (S) and (U) denote supervised and unsupervised setting. Motion -baed stylization is presented for both settings. Prob-style refers to probabilistic style space.
Table 6 presents the results of ablation experiments investigating various components of our latent stylization models. These components include stylization on the latent space (latent), the use of probabilistic style space (prob-style), homo-style alignment (homo-stylee. autoencoding, and cycle reconstruction. The experiments are conducted within the framework of Ours (A) and focused on the task of motion-based stylization. Results are reported on two datasets (Aberman et al.) (020) and (Xia al. 2015). It's important note that the dataset of (Xia et al.] 2015) is exclusively used for testing the generalization ability of our models and has not been used during training.
Overall, we observe notable performance improvement by incorporating different modules into our framework. For instance, our key designs-lated stylization and the use of a probabilistic style saace--igniffcantte enhance performance on the unseen (Xia et al., 2015) dataset, resulting in a 7% increase in stylization accuracy in the supervised setting. Additionally, homo-style alignment, despite its simplicity, provides a substantial performance boost across all metrics. Notably, content accuracy sees a remarkable improvement of 13% and 6% in supervised and unsupervised settings, respectively, underscoring the effectiveness of homo-style alignment in preserving semantic infor- mation.
In the subsequent sections, we delve into a detailed discussion of three other critical choices in model architecture and learning scheme: probabilistic (or deterministic) space for content and style features, separate (or end-to-end) training of latent extractor and stylization model, and the incorporation of global motion predictor.
Probabilistic Modeling of Style and Content Spaces. Table presents a comparison between de- terministic and probabilistic modeling approaches for both style and content spaces. In study, the
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Content Space
Style Space
(Aherman et al.
2020
Xia al.
2015
Style Ade
Style FID
Dis
Style Acet
Content Ade)
Concent FID
Geo Dis
D
D
0.9134007
0.022#002
0,50g
0.870
0.524
0.24g
0.767
P
0.945#007
0.020 0.020=002
0.344
0.926 :00s
0.674 2011
0.189
0.680
P
P
0.947
0.017
0.489
0,801 2008
0,417
0.322
0,708
Table Ablation study on the choice of probabilistic (P) or deterministic (D) space for content and style, in supervised setting. + indicates 95% confidence interval. Bold face indicates the best result, while underscore refers to the sccond best. Motion-based stylization is presented.
introduction of a probabilistic style space not only provides remarkable flexibility during inference, enabling diverse stylization and multiple applications. but it also consistently enhances performance and generalization capabilities. An intriguing aspect to explore is the impact of modeling the content space non deterministicallyy As highlighted in Tab. 7 we observe that probabilistic content space
achieves superior stylization accuracy in-domain datasets (Aberman et al. 2020). However, it
exhibits sub-optimal generalization performance on out-domain cases (Xia al. 2015).
(Aberman et al., 2020
Xia et al. 2015
Training Strategy
Style Ace
Style FID
Geo Disq
Style Ace"
Content Acc]
Content FIDT
Gea J
Sepurately
0.945 2007
0.02 0.020
0,344
0,926 #008
0.674#011 0.203
0.189 1006
0.680
#002
End-to-end
0.125=010
1.521 #024
0.577
0.174=014
1.4170000
0.700
Table 8: Ablation study on separately or end-to-end training the latent model and stylization model, in supervised setting. + indicates 95% confidence interval. Bold face indicates the best result, while underscore refers to the second best. (S) and (U) denote supervised and unsupervised setting. Motion-based stylization is presented.
Separate / End-to-end Training. Our two-stage framework can alternatively be trained in an end- to-end fashion. We also conduct ablation analysis to evaluate the impact of such choice of training strategy. The results are presented in Table g In practice, we observed that end-to-end training posed significant challenges. The model struggled to simultancously learn meaningful latent motion representation and effectively transfer style traits between stages. Experimental results align with this observation, revealing that stylization accuracy is merely around 15% on both datasets in the end-to-end training scenario, in contrast to the accuracy of 92% achieved by stage-by-stage training.
Aberran et al. 2020
CMU Mocap (CMU)
(Xia et al. 2015
Method
Style Acc|
Foot Skating!
Style Acef
Foot Skating.
Style Ace
Foot Skating
Ours (S)
0.945
0.130 1001
0.91840007
0.1404001
0.926
0.263
Ours w/o GMP (S)
0.942#003
0.141
00920=006
00160=001
0.882+005
0.331
Ours (U)
L010
0.102
0.828 1.10
1.09
0.860-010
0.179 4002
Ours wo GMP (U)
0.8174013
0.116-001
0.02
0. 0.122 0.1224001
0.777 1018
0.307
Table 9: Ablation study on global motion prediction (GMP, see Sec. 3.2.3p. The symbol indicates the 95% confidence interval. Bold indicates the best result. (S) and (U) denote supervised and unsupervised settings, respectively. Results of motion-based stylization are presented. Foot skating is measured by the average velocity of foot joints the XZ-plane during foot contact.
Global Motion Prediction (GMP). The primary objective of our global motion prediction is to facilitate adaptive pacing for diverse motion contents and styles. As illustrated in Tab. I0 we quan- tify the mean square error of GMP predicting root positions across three test sets, measured in millimeters. Notably, even on the previously unseen dataset Xia et al. (201)) the lightweight GMP performs admirably, with an error of 57.7 mm.
To assess the impact of GMP on stylization performance, we compare against contrast setting (Ours w/o GMP), where global motions are directly obtained from the source content input, akin to previous approaches. Additionally, we introduce a foot skating metric to gauge foot sliding artifacts, calculated by the average velocity of foot joints on the XZ-plane during foot contact. Table 9 show- cases motion--ased results (Aberman al. 2020: CMU Xia et al. 2015)) test sets. Across all comparisons, our proposed GMP effectively mitigates foot skating issues. Although 2-dimensional global motion features constitute only small fraction of the entire 260-dimensional pose vectors,
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Aberman et al 2020
CMU Miccup (CMU)
Xia et 2015
462
48.7
57.7
Table 10: Mean Square Error of Root Position Prediction. The metric is measured in millimeters. Note the dataset (Xia 2015) untouched during the training of the global motion predictor.
it makes considerable difference on the dataset of (Xia et al. 2015), improving the stylization ac- curacy by around 9%. In our 3rd and 4th supplementary videos, we also illustrate how our GMP enables adaptive pacing in different stylization outcomes (label-based and motion-basedo for the same content.
B FEATURE VISUALIZATION
Stitteg
Riad
Staney
Oipreseed
US
Peart
(a)
by [left], and Style (right).
Style by Style
Figure 8: Latent Visualization. Panel (a) displays the projection of the identical set content codes onto a 2D space using t-SNE, colored according to content labels (left) and style labels (right). This visualization sug- gests that content codes faithfully capture content traits, while style information has been effectively removed. In panel (b), style codes projected onto 2D space using t-SNE and colored by their corresponding style labels. Notably, clear style clusters emerge unsupervisedly, aligning with style labels.
Given that our content encoder accommodates motions of arbitrary length, we extract content codes from the Xia al. (2015) dataset. This dataset, unseen by our models, provides annotations for both style and content labels. Notably, the motions in this dataset are usually short, typically within 3s, which insufficient our style encoder. Therefore, for style codes, we takes the motions from dataset (Aberman et al. (2000) for visualization. The models are learned in unsupervised setting, using VAE latent model.
Content Code Visualization. Figure g (a) visually presents 2D projections of our content codes. The content codes are colored by their content labels on the left and by their style labels on the right. To generate these projections, the temporal content codes are aggregated along the tempo- ral dimension and then mapped to 2D space using 1-SNE. When the content codes are colored by content label (e.g., walking, kicking), distinet clusters aligned with the corresponding labels become apparent. However, when the same set of content codes is colored by their style label, these labels evenly distributed within these clusters. This observation suggests that the content code adeptly captures the characteristics of various contents while effectively erasing style information.
Style Code Visualization. Figure (b) visualizes the style codes in 2D space, color-coded by their style labels. Notably, these style labels were never used during model training. In contrast to the content code visualization in Fig. (a), the projected style codes exhibit strong connection with the external style label annotations. This observation underscores the effectiveness of our style encoder extracting style features from the motion corpus.
C Loss WEIGHT ANALYSIS
Tab. presents more quantitative results of our models on (Aberman al. 2020) and (Xia al. 2015 test sets. Specifically, we provide the ablation evaluations in both supervised (S) and unsuper- vised setting (U). For supervised setting. conduct experiments on also compares the diversity; and for unsupervised setting we adopt motion-based stylization. Note the base models are not necessarily final models, here they only for reference.
stylization which
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Aberman et al. 2020
2020
Xia el. 2015 2015
S/U
Azge
Ad
Abea
Style Acc|
Gico Dist
Divi
Style Acci
Content Acc1
Content FID]
S (base)
0.1
0.01
0.1
0.937200s
0.415
0.153
0.913
0.6692013
0.2022006
0.5
0.916644ms
0.3694000
0.091 2011
0.9244007
0.706-010
0. 1.1977007
0,001
0.962
0.4292001
0.125
0.933
0.619
0.192
0.1
0,940
0.414
0.141
0.914
0.634
0,209
0.01
0.955 1:006
0.41g
0.107
0.957
0.609
0.207
1
0.880
0. 0.4234000
0.3024020
0.833
0.625
0,236
(base)
0.01
0.1
0.804-011
0.441 4003
0.814
0.58g
0.217
0.790
0.489
#012
0.567#016
0.224=007
0.6594mm
0.430
0.701
0.619
0.190
0.01
0.669
3888 3888
0.671
0.641 #012
0,206=006
0.1
0.739
0.421p
0.762
0.619
0.214
Table 11: Effect of hyper- parameters of ours (A) the (Aberman et al. 2020) and (Xia al. 2015) test
sets. + indicates 95% confidence interval. Bold face indicates the best result, while underscore refers to the second best. (S) and (U) denote supervised and unsupervised setting. For (S), we present results of label-based stylization; and for (U), we present motion-basee stylization.
Effect of Musa Homo-style alignment ensures the style space of the sub-clips from one motion quence to be close to each other; is an important sulf-super signal our approach. Increasing the weight of homo-style commonly helps style modeling (style accuracy) and content preservation (content accuracy, FID), which however also comes with lower diversity. A common observation is that the performance style and content always contradicts with the diversity. It could be possibly attributed to the inherently limited diversity in our training dataset (Aberman et al. 2020), which is collected by one person performing several styles.
Effect of Apt- As weighs how much the overall style space aligns with the prior distribution (O, I). Smaller As usually increases the capacity of the model exploiting styles, which on the other hand deteriorate the performance on content maintenance and diversity.
Effect of Aeye- Cycle reconstruction constraint plays an important role unsupervised setting. In supervised setting, strong cycle reconstruction constraint is detrimental to style modeling. In contrast, while learning unsupervisedly, strengthening the cycle constraint enhances the performance on style transferring, and at the same time compromises the preservation of content.
(Aberman et 2020
Xia et al.
2015
An
Aema
MPJPE (Recomy)
Style
Style FID,
MPJPE (Recon).
Style
Content Acc?
Content FID,
0.001
0.001
39.4
0.915
0.020
62.5
0.926 tike
0.67
0.18g
0.1
0.1
360.
0.862
0.011
431.8
0,801
0. 0.58
0.276
0.01
0.01
180.4
0.873
0.011
250.5
0.830
0. 0.65
0.214
0.0001
0.0001
77.6
0.85
0.042
130.9
0.901 #011
0.661 #o1a
0.23
Table Effect of hyper-parameter: autoencoder on (Aberman et al. 2020) and (Xia et al. 2015) test
sets. + indicates 95% confidence interval. Bold face indicates the best result, while underscore relers to the second best. Results of motion-based stylization in supervised setting presented. MPJPE is measured in millimeter.
Effect of Autoencoder Hyper-Parameters In Tab. 12 we investigate the impact of autoen- coder hyper-parameters (An and Asms) on both motion reconstruction and stylization performance. Specifically, An encourages sparsity in latent features, while Ayms enforces the smoothness of temporal features. Through experimentation, we identify an optimal set of hyper-parameters with λu 0.001 and Xams = 0.001, which yields optimal performance in both reconstruction and styl- ization tasks. Notably, imposing excessive penalties on smoothness and sparsity proves detrimental to the model's capabilities, resulting in lower reconstruction quality. Additionally, observe substantial correlation between reconstruction and stylization performance, indicating that better reconstruction often translates improved stylization.
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D IMPLEMENTATION DETAILS
Our models are implemented by Pytorch. Motion encoder & and decoder D consists of 1-D convo- lution layers; global motion regressor 3-layer ID convolution network. The content encoder E, and style encoder E, are also downsampling convolutional networks, where style encoder contains average pooling layer before the output dense layer. The spatial dimensions of content and style code are both 512. Detailed model architecture is provided in Figs. a [0] The values of Veta. An and Aams are all set 0.001, and dimension D2 of z 512. During training our latent stylization network, the value of Anea Acye and Agı are (1, 0.1, 0.1) and (0.1, 1. 0.01) in supervised setting and unsupervised setting, respectively.
D.1 MODEL STRUCTURE
The detailed architectures of our motion latent auto-encoder and motion latent stylization model illustrated in Figure and Figure I0 respectively, where "wlo N", "IN" and "AdaIN" fer to without-Normalization Instance Normalization and Adaptive Instance Normalization oper- ations (Huang & Belongie 2017). Dropout and Activation layer are omitted for simplicity.
D.2 DATA PROCESSING
We mostly adopt the pose processing procedure in (Guo et al. 2022a). In short, single pose is represented by a tuple of root angular velocity, root linear velocity, root height, local joint positions, velocities, 6D rotations (Zhou et al. 2019) and foot contact labels, resulting in 260-D pose represen- tation. Meanwhile, all data is downsampled to 30 FPS, augmented by mirroring, and applied with Z- Z-nomalization.
Motion VAE
Tyweryle Der
Uperrple
Cop
WAW
D=256
384
512
512
512
512
512
384
256
512
Detailed architecture our VAE based motion latent model. The AE based latent model keeps only one convolution branch before the latent space. All convolutions, except the last layer of encoder, decoder and generator, use kernel size of
Figure 9:
D.3 BASELINE IMPLEMENTATION
For fair comparison, adapt the baseline models with minimal changes from their official imple- mentations, training them on the same data splits. More specifically, without violating their design of input representation and networks, all the re-implemented baseline methods strictly load the same preprocessed data for training.
AAberman al. 2020). Due to the intentional dual representations for style and content inputs in Aberman et al.] 2020), we make some modifications in the dataloader. We first recover the raw 21- joints structural motion data from the preprocessed data, and convert them into 84-D rotation-based content feature along with 4-D global motion, and 63-D position-basee based style feature, using their motion parsing function. addition, we modified the channels of network input/output layers to fit the adapted data. Since experiments solely consider style from 3-D motions, disable the 2-D branch as well as the related loss functions. However, we suffer from extremely unstable training process and poor results using the same hyper-parameters. It may result from the length extension
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Motion Latent Encoder
Motion Latent Decoder
Content Encoder
Generator
Uparpee
Oer
20
Can
Cape
20
One
AdU
AAINN
AdUN
512
768
512
512
512
768
1024
768
512
Style Encoder
Cor
512+16
vo.
N(u',
AdaNN
512
768+16
768
512
512
512
Scale A Shit
dee 1000
Coar
Sods
Tirvar
512
Figure 10: Detailed architecture of our motion latent stylization model in supervised setting. In unsupervised setting, the style label input is dropped. All convolutions, except the last layer of encoders and generator, use kernel size of 3.
of motion sequence (now 160 vs original 32) and inherent flaws of GANs (Zhu et al. 2017 Karras ict al. 2019). Thus, empirically, we lower the coefficient for adversarial loss Cade from to 0.5, and update the frequency of discriminator training from per-iteration to 0.2 per-iteration.
Inatructions:
We provido animmlt saiinatonsceddaaa ("No.1, No.2" ) comperison. These animations are ge generatid by injectng the style from Styte Ref into CONTENT Molion your profemed Please consider following reports mokind your derinion
Anumet on BUUST tistly bo natural Examptos of unnoture animatio are: feot slicingg ghoeting. twisted poses and welrd motion. Ant preserves content the CONTENT Motion
contains cverall style Styla Label shown the).
Pomoliation submiilte
sulomstically Short Cut Sintlerme
CONTENT Motion
STYLE Ref
No.1
No.2
Select aption
No
Figure 11: User study interface Amazon Mechanical Turk.
(Par al. 2021). We extract 63-D joint position feature and 126-D joint rotation feature from preprocessed data, catering for the designated dataloader in (Park et al.) 2021). Mcanwhile, we replace the original 4-D quaternion with the equally functional 6-D rotation (Zhou et al. 2019) without any loss of capability. Their model design is limited to fixed motion length, due to the scalable linear layer. Therefore, during evaluation on (Xia et al., 2013) test set, we duplicate the sequence meet the 160-length setting and then extract the corresponding result from the output.
(Jang et al., 2022) takes the motion representation building from per-joint's 6-D position-basee fea- ture (i.e. position, velocity) and 6-I) rotation-based feature (i.e. upward direction, forward direction)
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which is almost coherent with preprocessed data. Thus, we directly re-organize data to serve the baseline (Jang et al. 2022). keeping everything else unchanged.
E EVALUATION METRIC
Why certain metries are not used across all datasets? Given our latent stylization models are trained on (Aberman al. 2020), CMU Mocap (CMU) and (Xia et al. 2013) aims emphasize zero-shot performance on Style Precision and Content Preservation respectively. Style classifier is trained on (Aberman et al. 2020), where all style motions come from. Compared to (Aberman) al. 2020) and CMU Mocap, (Xia et al. 2015) is quite small (570 clips), comprising variable- length short motions (< 3s). Style FID isn't I computed for (Xia et al. (0015) due to substantial length differences between the style motion (from (Aberman et 2000), 5.3s) and output motion (< 3s). Content classifier trained only on (Xia et al.] 2015) to evaluate the Content Preservation as content labels are only available this dataset. Since there is evidence that this content classifier can generalize to other datasets, we only use it for (Xia et al. 2015).
F
USER STUDY
The interface of the user study on Amazon Mechanical Turk for our experiments is shown in Fig- ure [[]] Since motion style not as obvious as other qualitative attributes for common users, to simplity the study, we only compare one baseline result with ours each time. Moreover, for intro- duction, we briefly explain the concept of motion stylization, presenting the content motion as well as style motion for reference. Users are instructed to choose their preferred results over two gener- ated stylization results based on judgement on naturalism, content preservation and style visibility. This study only involves users that are recognized as master by AMT.
G INTERPOLATION
We present the results of interpolation in the respective style spaces learned unsupervisedly Fig. a2]a) and supervisedly Fig. b2... We are able to interpolate between styles from different labels in unsupervised setting. Specifically, two style codes are extracted from sneaky motion and heavy motion respectively. Then we mix these two style codes through linear interpolation, and apply them to stylize the given content motion. In supervised setting, the generator is conditioned on specific style label. Here, we interpolate styles between two random style codes sampled from the prior distribution (O, I). Stylization results are produced conditioned a common style label, heavy. From Figure [2] we can observe the smooth transitions along the interpolation trajectory of two different style codes. Please refer to our supplementary video for better visualization.
Figure 12: Stylc Interpolation. (a) Cross-style interpolation in unsupervis- edly learned style space. Styles are interpolated between style codes of sneaky (left) and heavy (right) motions. (b) Homo-style interpolation visedly learned style space. With style label heavy condition input, styles are interpolated between that randomly sampled from N/(0, I). One key pose for each motion is dis- played.
(a) Cross-style Interpolation
super-
style codes
(b) Homo-style Interpolation
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Figure 13: Failure cases. Top row shows content motion; bottom row shows our corresponding results. Stylization results of breaking dance motion (left) and push-up motion (right) using happy style label are displayed.
TARY
H LIMITATIONS AND FAILURE CASES
Firstly, our model may encounter difficulties when the input motion substantially deviates from our training data. Figure 13 presents two failed stylization results on rare content actions, i.e., breaking dance and push-up. Given that our model has only seen standing motions during training, it commonly fails to reserve the lower-body movements in these two cases. Interestingly, our model can still retain the general motions of upper-body.
Secondly, the underlying reason for different performance of ours(V) and ours(A) on for example, diversity, style and content accuracy, remains unclear.
Lastly, certain styles are inherently linked to specific content characteristics, particularly within the datasets of (Aberman et al. 2020, Xia al. 2013). For instance, styles like old, depressed and lazy typically relate to slow motions, while happy, hurried, angry motions tend to be fast. As styl- ization process aims to preserve content information, including speed, there could be contradictions with these style attributes. For instance, stylizing an slow motion with hurried style might not yield an outcome resembling hurried motion. We acknowledge this aspect for potential exploration in future studies.
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Reviews:
[{"review_id": "pXgDXPxULJ", "reviewer": "Reviewer_rvJ1", "summary": "This paper introduces an innovative framework for human motion stylization. The method breaks down the motion latent code into a content code and a style code. It leverages AdaIN to infuse style information into the semantic content, dictated by the content code. Furthermore, the authors introduce a new learning approach that encompasses reconstruction, cycle consistency, and homostyle alignment.", "questions": "Please kindly refer to the weaknesses mentioned above.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper demonstrates impressive quantitative and qualitative results, establishing a new state-of-the-art with a substantial margin.\n\n2. The proposed framework is novel. The associated conclusions and experiments make significant contributions to the research community.\n\n3. The paper is well-written, ensuring that its content is easily understandable for readers.", "weaknesses": "My main concern primarily revolves around the selection between probabilistic and deterministic methods when extracting the content code and style code. The authors should incorporate further discussion and analysis in the following areas:\n\n1. The authors should provide more intuitive explanations for why deterministic encoding is used for the content code while probabilistic encoding is used for the style code.\n\n2. The authors need to conduct an ablation study to validate their conclusions regarding this design aspect.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rU79OMbLIV", "reviewer": "Reviewer_rCKX", "summary": "The paper presents a new human motion stylization method based on a latent editing approach. The approach is based on a latent motion space which can differentiate the content and style of the motion. Based on the label or prior information of the style, the proposed approach can generate stylized motion results. The proposed approach is validated on several motion benchmarks like Aberman and CMU Mocap. Also, an user study is reported to further validate the performance of the approach.", "questions": "Please well address the questions in the weakness section.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The idea of generate stylized motion sequence is interesting and helpful in many industry applications. \n2. The proposed approach is well validated in several motion benchmark quantitatively. Also, a user study is provided to further validated the performance of the approach.", "weaknesses": "1. How to differentiate between the content and style of motion is not a trivial work. Usually, these two content is interleaved. The proposed approach seems to implicitly to decouple these two factors. Is it possible to provide a validation of the proposed approach can decouple these two content?\n\n2. For the experiments, the metrics may not well validate the performance of the proposed approach. For example, the style accuracy is based on a style classifier, which may be very accurate to capture the styleness of the proposed algorithm. Also, for case with a styled motion input, the transfer result based on the stylized input may not be easily verified. \n\n3. In the attribute edting literature, scholars are actively involved in the style edting, for example, to interpolate two style codes. Is it possible to generalize the generative ability of the proposed approach for more editing operation based on the latent space. \n\n4. How about the performance of the proposed approach on the long motion sequence? Is it possible to maintain the style for the whole motion sequence rather than a short period.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "O5LYeNKsP7", "reviewer": "Reviewer_bi3M", "summary": "The authors propose a generative method for motion generation. The key idea is using the motion latent code as a compact and expressive representation. The motion code is enforced to decompose into a deterministic content code and a probabilistic style code. The style code is expressed in a certain formula of distribution. And the generation is performed by an auto-encoder. The method can generate stylized motion in different schemes: unconditional or conditional to certain style label or exemplar motion. Also, the benchmarking results also suggest the good effectiveness and the time efficiency of the proposed method.", "questions": "Please see my questions above.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The proposed method can support two modes of motion generation: conditional to label of style and the by sampling from the prior distribution. This enables a diverse potential downstream applications.\n- The latent code of motion is shorter than the motion itself, making the leveraging of motion descriptor in the neural network more efficient.\n- The stylization and the content information can be disentangled from the input motion code, supporting versatile application of motion generation.\n- I find the idea of using three groups of input is interesting that requires no additional style label but can encourage disentanglement. Also, the cycle-consistency idea is also interesting.", "weaknesses": "- Probably not a “weakness”, but this is a unconvincing design to me that a small 1D convolutional network can predict the global motion (say root position) from local joint motion. I would ask for more details and evidence to support this design. The results shown in Table 7 only suggest relatively marginal influence, especially in the supervised settings. Is it possible to also have more results on other datasets?\n- Given multiple components and modules are proposed to construct the proposed method, a complete ablation study is expected to validate their effectiveness. The ablation of loss weights shown in Table 6 is probably not enough to convince readers that all proposed components are contributing to the final improvement of the observed benchmarking results.\n- Question: The results are focused on motion generation in the skeleton representations. Can the authors also propose some visualization with SMPL LBS? With the mesh, sometimes it can be more intuitive to estimate the plausibility of the joint angles.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "R2AujaEnFc", "reviewer": "Reviewer_fyTQ", "summary": "This paper tackles the problem of stylizing 3D human motions. To this end, the authors first train an autoencoder to bring the motion sequence into latent space and propose to decouple the style and content of the motion in latent space. Specifically, the latent code is mapped to content code and style space via two encoders and then mapped back to the original latent space via a generator equiped with adaptive instance normalization (controled by the style code).  During training, several losses are applied to ensure the disentanglement between the content and style including  the Homo-style Alignment loss, swap and cycle reconstruction loss.", "questions": "- It seems the whole model can be trained end-to-end. Why the motion autoencoder is pretrained? What will happend when every components are trained end-to-end?", "limitations": "", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The presentation is clear and easy to follow.\n- The overall pipeline is sound and the authors provide comprehensive experimental analysis on the performance of the proposed method.", "weaknesses": "- In terms of the pipeline itself, there are not much novelties. Every component in the pipeline is straightforward and proposed by previous methods for example the adaptive instance normalization, the cycle consistent reconstuction. Although the proposed homo-style alignment loss is new, it is quite simple.\n- More details about the \"Ours w/o latent\" model is needed.\n- It seems all the inline references used in the paper are not in parenthesis. I believe the authors miss used \\citet when there should be \\citep.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (poster)