MotionWeaver: Holistic 4D-Anchored Framework for Multi-Humanoid Image Animation
Authors: Xirui Hu
Yanbo Ding
Jiahao Wang
Tingting Shi
Yali Wang
Guo Zhi Zhi
Weizhan Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Character Animation
Diffusion Model
Video Generation
Primary area: generative models
TL;DR: MotionWeaver is a thoroughly 4D-anchored framework for multi-humanoid animation, enabling robust synthesis across diverse body forms, complex interactions, and severe occlusions.

Abstract: Character image animation, which synthesizes videos of reference characters driven by pose sequences, has advanced rapidly but remains largely limited to single-human settings. Existing methods struggle to generalize to multi-humanoid scenarios, which involve diverse humanoid forms, complex interactions, and frequent occlusions. We address this gap with two key innovations. First, we introduce unified motion representations that extract identity-agnostic motions and explicitly bind them to corresponding characters, enabling generalization across diverse humanoid forms and seamless extension to multi-humanoid scenarios. Second, we propose a holistic 4D-anchored paradigm that constructs a shared 4D space to fuse motion representations with video latents, and further reinforces this process with hierarchical 4D-level supervision to better handle interactions and occlusions. We instantiate these ideas in MotionWeaver, an end-to-end framework for multi-humanoid image animation. To support this setting, we curate a 46-hour dataset of multi-human videos with rich interactions, and construct a 300-video benchmark featuring paired humanoid characters. Quantitative and qualitative experiments demonstrate that MotionWeaver not only achieves state-of-the-art results on our benchmark but also generalizes effectively across diverse humanoid forms, complex interactions, and challenging multi-humanoid scenarios.

Published as a conference paper at ICLR 2026
MOTION HOLISTIC AD-ANCHORED FRAME- WORK FOR MULTI-HUT HUMANOID IMAGE ANIMATION
Xirui Hu Yanbo Ding Jiahao Wang', Tingting Shi', Yali Wang', Guo Zhi Zhi Weizhan Zhang
School of Computer Science and Technology. MOEKLINNS Lab, Xi' an Jiaotong University, Xi' an, 710049, China "Shenzhen Institutes of Advanced Technology. Chinese Academy of Sciences, Shenzhen, China IIntiittee of Artificial Intelligence (TeleAI), China Telecom, Beijing, China fxiruihu, uguisu, shit shitingting)ess. xjtu. ed,
dio.ding, yl. wangw ca, guozz2@chinateeecom.. zhangwzh@x edu.cn on
Reference
Target
Motion Weaver (Ours)
Existing Method
UniA Animate
Animate
MTVCrafter
Animmator
Human Vid
Renli-Durce
Figure 1: We propose Motion Weaver, novel framework for multi-humanne image animation, which effectively handles occlusions and complex interactions in multi-character scenarios, while showing strong generalization across diverse humanoid characters and artistic styles.
These authors contributed equally; work done during internship TeleAI. Corresponding author. Project leader.

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Published as a conference paper at ICLR 2026
ABSTRACT
Character image animation, which synthesizes videos of reference characters driven by pose sequences, has advanced rapidly but remains largely limited to single-human settings. Existing methods struggle to generalize to multi-humanoid scenarios, which involve diverse humanoid forms, complex interactions, and fre- quent occlusions. We address this gap with two key innovations. First, we intro- duce unified motion representations that extract identity-agnostii motions and ex- plicitly bind them to corresponding characters, enabling generalization across di- verse humanoid forms and seamless extension multi-humanoid scenarios. Sec- ond, we propose a holistic 4D-anchored paradigm that constructs a shared 4D space to fuse motion representations with video latents, and further reinforces this process with hierarchical 4D-level supervision to better handle interactions and occlusions. We instantiate these ideas in MotionWeaver, Weaver, an end-to-end framework for multi-humanoid image animation. To support this setting, we curate a 46-hou dataset multi-human videos with rich interactions, and construct a 300-video benchmark featuring paired humanoid characters. Quantitative and qualitative ex- periments demonstrate that Weaver not only achieves sfute-of-the-ase re- sults our benchmark but also generalizes effectively across diverse humanoid forms, complex interactions, and challenging multi-humanoid scenarios.
INTRODUCTION
Character image animation, which aims to synthesize videos reference character image driven
by pose sequences estimated from an input video (Zhu et al. 2023 Peng et al., 2025 Chang al., 2025), has gained significant attention due to its wide applications in film production. immersive
content, and e-commerce (Lin al. 2022 Lauer-Schmaltz et al. 2024 Islam et al. 2024 Song
ct al. 2025 Chamola et al., 2024 Qin & Hui 2023).
While existing works have demonstrated promising results in motion control and character consis- tency (Gan et al. 2025; Xie et al., 2024 Tu et al.) 2025b Guo et al.] 2024). they remain largely limited to single-human scenarios and fail to generalize to multi-humanoid settings characterized by diverse humanoid forms (e.g.. robots, anthroponorphic animals, game avatars), rich interactions, and frequent occlusions. These limitations of current methods can be attributed to three primary factors: (1) Deficient Motion Representations: Existing methods inadequately disentangle motion from morphology, as control signals (e.g.. skeleton maps or SMPL renderings) carry information about body proportions and shapes, which hinder generalization across diverse humanoid forms (Figure D fifth row). Moreover, they typically entangle control signals of multiple characters (e.g., multiple skeletons composited into one map), leading to identity confusion (Figure 1 top row), and limiting their applicability in multi-humanoid settings. (2) Lack of Explicit 4D Modeling:
Prior approaches direetly fuse video latents with control signals without explicitly modeling the spatiotemporal relationships among characters and with the scene (c.g., using naive cross- .rossoattention), therehy limiting their ability to capture complex nulti-humanoid dynamics (Figure 1 third row). In particular, the absence of depth information is a critical drawback, making it difficult to resolve occlusions (Figure 3 and [ bottom row) and distinguish between genuinely smaller body size and a greater camera distance. (3) Suboptimal Training Strategy: Current frameworks commonly rely on 2D pixel-wise MSE loss, which provides no explicit supervision for 4D motion and inadver- tently couples motion with appearance, impairing motion understanding and leading to overfitting to human-speen -uman-specific visual features (Figure [] fourth row). Collectively. these issues push conventional models to act as a mindless renderer of specified control signals rather than real generator guided by motion information.
To address these limitations, we argue that models should be guided by more universal and scalable motion representations and trained within a thoroughly 4D-grounded perspective, thereby fostering a true grasp of motion dynamics to strengthen generalization. Guided by this principle, we intro- duce Motion Weaver, a framework built upon three innovations: (1) Unified-Chon Sefffed-Chhreograhhhy Core (UCC): extracts ddentity-annosie motion signals and associates motion signals with corresponding driven characters to form unified motion representations, which robustly separate motion from mor- phology and enable applicability in multi-hummnnid scenarios. (2) Hyper-Scene Integrator (HSI):
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models shared 4D space fuse video latents and motion representations, enabling the resolution of frequent occlusions and dense interactions in hulli-hummaoid settings. (3) Hierarchical-4D Su- pervision (H4S): couples occlusion supervision at high-noise steps with motion-level supervision at low-noise steps, providing 4D motion supervision and mitigating overfitting to human appearance. Overall, the contribution of Motion Weaver lies in introducing novel unified motion representa- tions and formulating a holistic 4D-anchored paradigm where motion extraction, lotion-latent fusion, and training supervision are consistently grounded in 4D space. Building on these designs, Motion Weaver constitutes an end-to-end framework that supports multi-humanoid animation and robustly addresses interactions and occlusions.
To enhance the training of our model, we further curate dataset containing 46 hours of multi-humai videos, referred to as MultiHuman46,, which features diverse interaction patterns and scenes. Addi- tionally, we introduce DualDynamics, a benchmark of 300 videos, each showcasing two humanoid characters engaged interaction-rich scenarios. These videos have undergone a rigorous filtering process to ensure quality. Quantitative and qualitative experiments demonstrate that Motion surpasses state-of-the-art methods, showcasing its generalization ability, identity preservation, and motion consistency in multi-humanode scenarios. Our main contributions are summarized fol- lows:
We propose MotionWeaver, a novel framework built upon the unified motion representa- tions and the 4D-anchored paradigm, designed for multi-humanoid image animation in- volving diverse humanoid forms, rich interactions, and frequent occlusions.
We introduce UCC to obtaín unified motion representations, HSI and H4S effectively construct shared 4D space for fusing motion representations with video latents.
We curate the MultiHuman46 dataset, which encompasses 46 hours of multi-human videos, and create DualDynamics, a benchmark comprising 300 videos of multiple humanoid char- acters in interaction-rich scenarios. Extensive experiments demonstrate that MotionWeaver surpasses state-of- of-te-of-thh-an the-art methods in nulti-huumanid scenarios.
2 RELATED WORK
2.1 DIFFUSION TRANSFORMERS
Diffusion Transformers (DiT') replace the traditional U-Net architecture with a Transformer model to denoise latent representations (Peebles & Xiel 2023 Xing et al. (025) Recent advances com- bining DiTs with rectified flow techniques (Kong et al. 2024 Zheng et al. 2024 Yang et al. [025] Blattmann et al. 2023) have achieved state-of-the-art generation quality. During inference, the model first establishes the overall structure, then progressively refines fine-grained details (Cao et al.,
2024). Our training framework will be specifically designed to leverage this characteristic.
2.2
CHARACTER IMAGE ANIMATION
Recent research has shifted from GAN-based approaches (Siarohin ct al.
202
2018 Huang al.
2021
Siarohin et al. 20216) to diffusion-based methods (Chang et al.
2024 Wang et al.) 2025a
Karras al.) 2023 Zhou al.
2024 Ma et al.]
2024al Li et al., 2025| Xu et al. 2024a Hong
et al. 2025 Hu et al. 20255) for image animation. Many methods employ skeleton maps to provide
precise motion guidance (Zhang al. 2025 Tu al.
2024 Tan et al.) (025). Subsequent works
further enrich the intermediate signals by incorporating mesh renderings (Zhu et al. 2024 Huang
2024), dense pose maps WWang et al.
2024a|
Xu al. 2024b; Yoon et al.
2024
) facial
expression controls (Luo et al. 2025 Ma et al. 2024b
et (025b), and hand controls (Pavlakos
al.
2024 Zhou et al. 2024). Further extensions include methods that integrate humans into
given scenes to achieve scene affordance (Hu et al. [025a] Kulal et al. 2023 Ostrek et al.) 2024
Parihar et al. 2024), as well as techniques that model human-object interactions (Xu et al. 2025a
Men et al. 2025). Additional research explores camera embeddings for controllable viewpoints
and camera motion (Shao et al. 2024; He et al.] 2025 Hou Chen 2025 Yang et al. 2024b).
Nevertheless, these approaches remain constrained single-character scenarios and rely primarily on 2D control signals, which inevitably discard valuable cues from the real 4D world. Recent works
have begun to incorporate 3D control signals. MTVCrafter (Ding al. 2025) achieve lossless
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information control by encoding 3D motion data. However, the inherent feature fusion mechanism and static RoPE encoding scheme are insufficient to address challenges specific to multi chalaic settings, such position swapping and frequent occlusion. Efforts toward multi-character scenarios remain highly limited. While Fance'Togethe and Structural Video Diffusion (Chen et al., [0255] Wang al. 2025c) tackle some of the challenges in multi-human scenes, they heavily rely accurate human masks as control conditions. Although this approach effectively binds motion and corresponding identity, it requires strict alignment between reference characters and driving videos and cannot be readily extended to mullti-uumanidd settings. To address these limitations, we propose Motion Weaver, a framework that extracts more universal and scalable representations and built entirely from a 4D perspective for animating multiple characters with diverse humanoid forms and complex interactions.
METHOD
Enfficl-Chorreeepppy Core
global
(b) Hyper--Scnee Integrator
SMPL
positice
Occlasion (Kopc)
MLP
Delecur
Pose
query
querry
2000 mn
Cross
latents
dynamically solect
Mapper
Mosian Takenizer
Attention
key
mánn uny
z-aware
umsrotinn
Key
QK sharal
Group
value
video Iatents
Zuii
(c) Mieenchhccuf-4
Supersision
reference fruene
Timestea
>
Cuse + Caye
Adal & Gate
Adal Gate
SelfAnn
Layer Norm
Text Text Cross Arto
AdaLN
FPN
Truh
video Iatenbe
IsaT
Nake
Figure 2: The overview of our Motion Weaver. (a) Unified- Sifffed-Choreographyy Core extracts unified
motion representations (2mm)) ) (b) Hyper-Scent Scene Integrator integrates the motion representations with video latents within shared 4D space. (c) Hierarchical-4D Supervision utilizes timestep-specific 4D supervision to help the model effectively learn motion representations.
Our task is animate multiple humanoid characters based on a reference image and driving video containing desired poses while training exclusively on human datasets, which requires more ingenious design. After presenting the preliminaries that underpin our approach (Section 3.1) our method revolves around two key contributions: (1) we introduce unified motion representations via the Unified- SifffeddChhrrographhy Core (Section 3.2). and (2) we establish holistic 4D-anchored paradigm, which constructs shared 4D space to integrate these 4D-informed motion representations into the base diffusion model through the Hyper-Scene Integrator (Section 3.3). while providing 4D-level guidance via our proposed Hierarchical-41 Supervision (Section 3.4). Finally, the training and eval- benchmark,
uation are facilitated by our curated dataset, MultiHuman466, and the DualDynamics (Section 3.5
3.1 PRELIMINARIES
SMPL (Skinned Multi-Person Linear) is a parametric 3D body model that represents the human surface as a mesh (Loper et al. 2015). It factorizes body variation into: (1) Shape parameters (8), which define individual body proportions and build; (2) Pose parameters (#), which represent relative joint rotations for the 24-joint kinematic skeleton; and (3) Translation parameters (T), which specify the global position in 3D space. By applying linear blend skinning and pose-dependent offsets
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to template mesh, SMPL provides compact yet expressive representation for driving character motion.
RoPE (Rotary Positional Encoding) has been widely adopted in self-attention mechanisms (Su et al, 2024). Unlike traditional positional encoding, RoPE continuously rotates the position-dependen embeddings, allowing the model to preserve the relative distance between tokens more effectively. Mathematically, RoPE represented as:
(1g,))( m) R W (g.k).mm
where xm represents the m-th token, W (q.b) denotes the learnable projection matrices, and R E Raxd is the rotary matrix corresponding to the m-th position. The rotary matrix is defined by pre- determined parameters O
3.2 UNIFIED- CHOREOGRAPHY CORE
To obtain the unified motion representations Zuni that generalize across diverse humanoid forms and remain applicable to multi-humanoid scenarios, we introduce the Unified-Chhoregraphhh Core (UCC), which extracts adentity-agnoste motion tokens and associates them with the corresponding characters.
Extract motion tokens for diverse humanoid forms generalization. Unlike prior works that sim- ply extract skeleton maps or SMPL rendering motion representations, where appearance cues are inevitably entangled and thus impair generalization diverse humanoid forms (Wang et al./ 2002a]] Zhu et al.) 2024). we design series of decoupling mechanisms to explicitly remove appearance information and distill identity-agnostic motion tokens. Specifically, we first employ a pose detector to extract the SMPL parameters of the characters in the driving video and convert them into joint coordinates, yielding E Px as the raw representation. Here, P and F denote the number of persons and frames, respectively. To further eliminate appearance rellatee biases such skeleton length and human-specific traits, we map these coordinates onto a standardized skeleton p. where fixed Euclidean distances between adjacent joints enforced, resulting in standardized representa- tions X. In order to suppress frame-level noise and yield highly generalizable motion tokens 2mos we design task-specific tokenizer 2,0k, whose design is inspired by prior research (Ding al. 2025). Specifically, Po downsamples X along the temporal axis to produce motion tokens. This overall procedure can be formally expressed as:
Amo = P (Map(x,p) p)) E PPX(TXJ)x
where T denotes the latent temporal length. Further information regarding the standardized skeleton P can be found in Appendix G
Bind motion and character for lead to identity confusion in hulti-humanoid settings, particularly when characters exchange posi- tions. To address this issue, we explicitly bind motion to characters. Our approach builds upon pre-trained 12V model, where reference frame features provide strong guidance throughout the gen- eration process (Wan et al., 2025). Leveraging this property, we first segment the driven characters from the reference frame using masks, and then feed them into the base model's VAE and patchify- ing module to extract appearance features, yielding identity tokens Zu E LxX,,, where L is the sequence length and is the feature dimensionality of each token. We then introduce a group atten- tion module that associates the identity-agnn agnostiic motion tokens Zmo with the corresponding identity tokens Zuz, producing the unified motion representations Zuni E PR J)DD, For each character, the motion tokens serve as the query, while the identity tokens act as both the key and the value:
applicability. Prior motion representations often
GroupAttn Q (Zmooj)) ), K (2 ((Zi())) (Txx))
[mmipp]
where Smmipp denotes the unified motion representations for the p-th person. The group attention module is composed a cross cross-attention layer followed by an MLP, both enclosed within residual connections.
3.3 -CCEN INTEGRATOR
While the unified motion representations provide reliable character-level signals, effectively fus- ing them with video latents in multi-hummnoid settings is difficult due to dynamic spatiotemporal
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positions, dense interactions, and occlusions. We argue that explicit 4D spatiotemporal model- ing, particularly of depth cues, is essential to resolve these challenges. To this end, we propose the Hyper-Scene Integrator (HSI,, which aligns and fuses motion representations and video latents within a shared 4D space to guide generation.
A critical aspect of this integration is specifying the 4D position of motion representations. Thus, define a motion-unit Euuifa.t E IR (R which represents the motion of character p at latent timestep t. We then extract the SMPL translation parameters (æ, y, 2) corresponding to this motion, compute their temporal average, and augment them with the latent timestep t. This yields V [y.d] E Rt, which specifies the global position of each motion-unit. To effectively construct a shared 4D space, we note that depth information, compared with the other three dimensions, is both erucial and more difficult for the model to capture, and that video latents inherently lack explicit depth cues. To this end, HSI introduces two complementary mechanisms: (1) a depth-aware attention that models the z-axis under occlusion-loss supervision, ensuring correct depth ordering and visibility; (2) a dynamic C- RoPE that models the axes (t,, x. y), providing direct positional cues that allow the model to rapidly capture spatiotemporal relationships.
Depth-aware attention for axis z. To encode depth information, we extract the corresponding 2- coordinate from [pp. IR' for each motion-unit Funi[p.t]] E TR-'DD and encode it with a sinusoidal embedding obtain the depth token 2dewablp.1] R.. Each token of motion-unit is then concatenated with the depth token along the channel dimension, and processed by the MLPk to generate a 2- aware key. For each video latent =u[t,a.u]+ another ML.P is applied obtain the z-aware query:
K kpp... = MT.P AT.Px((=m(pp...)) || #depth(p.a]) E ETR,
(5) To supervise the learning of the depth-aware MIPs, we first obtain the final attention score ma- trix H E [0, []TTHW)((PT)) from the cross-attention module. For the p-th character, take the corresponding slice along the P dimension and sum over the last two dimensions (T and J), pro- ducing a character-specific map h, E [0, 1]æTH)) Collecting the maps of all characters yields (h:) P-1 E [0, PX Then, we construct ground-truth masks {m; P-1
F = MLP (Z E RR.
[ E {0, Px (TTHW), In regions where characters overlap, the mask value is enforced be zero for the character that lies farther along the depth axis, thereby explicitly encoding the occlusion relationship. To ensure accurate depth learning, we minimize the following occlusion loss:
Locc TP Σ MSE(h;, m;),
which enforces the model to learn the depth-aware occlusion relationships correctly.
Dynamic C-RoPE for axes (1,,,y), y)- To effectively construct shared space for modeling positional relationships among characters and with the scene, we first build the corresponding rotary matrix set for dynamic C-RoPE (Cross-Attention Shared RoPE), denoted as Φ E (T (xTxxX)))(DxD) The rotary matrix at any point (f, x. y) is given by:
R
0
Rdd
-...t.g
d/3 R
R
0
R 4/3
0
R.e.y/
where R E 5 represents the rotary matrix associated with the i-th position in the respective dimension. Since the (a, indices of frame latents reside in the pixel space, whereas (x, y) w are defined in the camera space, we further project (x, y) E // onto the same perspective plane for alignment. Subsequently, the rotary matrix is dynamically selected from o based on the global position of each motion-unit to rotate the key, while the query is rotated using its associated rotary matrix:
kp...j = Ro (p.. Kp.t..
(8)
-t...y = Rd R. Ft.
(9)
To further clarify, we provide code snippet in Appendix N The final step involves projecting the unified motion representation Zimi to obtain the corresponding value, after which cross-ate attentiotn is applied to fuse the video latents with the unified motion representations within shared 4D space.

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3.4 HIERARCHICAL-4D SUPERVISION
Since HSI has already established a 4D space for integrating 4D-informed motion representations, further introduce Hierarchical-4D Supervision (H4S) to regulate the model's learning of occlu- sions and interactions, thereby establishing a holistic 4D-anchored paradigm. H4S provides 4D motion supervision decoupled from appearance to enhance the efficiency of motion learning and reduce overfitting to human appearance. This design is also aligned with the inherent characteristics of diffusion models, which initially learn global layout and structure during the early, high-noise de- noising steps before progressively focusing on finer details in the later, low-noise steps (Yang et al.) 2024a Chen et al. 2024). During training, H4S utilizes a dynamic loss function that adapts to the current noise level, which is defined as follows:
LMSE + Xooce, 12 aT {
C
(10)
LMSE + XeC t<aT
where t represents the current timestep sampled during training, with T being the total number of timesteps. The loss function switches between auxiliary components based on predefined thresh- old a. For high-noise timesteps, we apply a standard Mean Squared Error (MSE) loss LMSE. in conjunction with the occlusion loss Loce in Eq. (6). which ensures that the model establishes accurate occlusion relationships between characters from the very beginning of the denoising pro- cess. For low-noise timesteps, the occlusion loss is replaced with motion-level loss LMO. At this stage, the model can already predict the denoised latents with near-accurate precision. These denoised latents are subsequently decoded by the VAE and passed through the stem of pre-trained 4D pose detector. The extracted motion features are then aligned with their ground-truth using an MSE loss, forming LMO. which transfers strong 4D motion priors into our HSI. Further details on the extraction of motion features are provided in Appendix [ In addition, we introduce trainable dumestep-conditions AdaLN and gating mechanisms for modulating HSI, which endow the model with stronger timestep sensitivity and finer control over the 4D feature fusion process.
3.5 MULTIHUMAN44 DATASET AND DUALDYNAMICS BENCHMARK
To facilitate the training, we curate MultiHuman46,, a 46-hour multi-human video dataset that en- compasses wide range of interaction patterns and scenes (e.g.. boxing. fencing, and dancing). The clips are collected from public datasets, web-crawled materials, and carefully screened Al-generated videos. We estimate SMPL parameters using a state-of-the- eet al. 2025) and apply a multi-stage filtering pipeline to retain only video clips that exhibit strong temporal consistency, high motion fidelity, and reliable SMPL. parameter accuracy.
pose detector (Newell
Given that publicly available character animation benchmarks primarily focus on single-human set- tings, such Fashion (Zablotskaia et al.) 2019) and TikTok (Jafarian & Park (021), we aim to fill the gap in benchmarks for multi-humanoid image animation. To this end, we construct DualDynam- ics, novel benchmark comprising 300 videos, each depicting interactions between two humanoid characters. DualDynamics is purposefully constructed to rigorously assess models' understanding of motion under diverse humanoid forms and complex interactions. All benchmark videos are pro- duced by a professional animation team and undergo filtering to ensure rich character interactions. Further details on the dataset and the benchmark are provided in Appendix] []
4
EXPERIMENTAL RESULTS
4.1 EXPERIMENTAL. SETUP
We employ CoMotion Newell et al. 2025) to estimate the SMPL parameters of each individual, and further use these parameters to construct the corresponding masks. As a sfate-of-the-an method for multi-person pose detection under frequent occlusions and interactions, CoMotion provides robust depth and pose predictions that yield reliable motion signals for our pipeline. We first train a robust 4D tokenizer. We then adopt Wan2.1-12V4 14B-11BB4 -800 (Wan et al. 2025) as the base model and insert our HSI into it at regular intervals of every four blocks. At this stage, all parameters are kept frozen, while only our group attention module and HSI are trainable. We set a and X 1, and choose a = 0.6, with the rationale provided in Appendix(H) The input video clips consist of 49 consecutive frames. Optimization is performed using the AdamW optimizer with B1 0.9, B2 = 0.99. weight
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Reference
Target
Motion (envernnenv)rOurrs)
Existing Method
MussPusc
HumanVid
Aniimte--
Figure 3: Qualitative Comparison with Existing Methods. The yellow and red meshes indicate the target motions of the left and right characters from the reference image, respectively. Our MotionWeaver method achieves superior identity preservation and motion accuracy for multiple humanoid characters. Notably, it is the only approach that correctly handles dense inter-character interactions and occlusions.
decay of 1 10 2, and a learning rate of 2 10 Training is conducted on 8 NVIDIA H100 80G GPUs with a batch size of per GPU. To reduce memory consumption and enable more efficient training, we present set of tailored training strategies, detailed in Appendix
4.2 SOTA COMPARISON
Baselines. We evaluate our method with leading and open-source baselines, including RealisDance- DiT (Zhou al., 2025), UniAnimate-DDiT (Wang et al., 2025b). Animate-X (Tan et al. 2025), Hu-
manVid (Wang et al.] (024b), StableAnnimator (Tu et al. 2025a). MusePoseeTong et al.
2024 and
MimicMotion (Zhang al. 2025).
Quantitative Evaluation. The results are reported in Table 1 covering nine widely used metrics: LI, PSNR (Horé & Ziou, 2010), SSIM WWang al. 2004). ) LPIPS (Zhang al. 2018), DISTS DDing et al. (0022), CLIP (Hessel et al. 2021), FID (Heusel et al.) 2017). FID-VID (Balaji al., 2019). and FVD (Unterthiner et al. 2019). On the DualDynamics benchmark, where each video composed of two humanoid characters, MotionWeaver consistently outperforms all the baselines across all the metrics. This clearly demonstrates the superiority of our approach multi-humanoid scenarios characterized by rich interactions, frequent occlusions, and diverse humanoid forms.
Qualitative Evaluation. The comparisons are provided in Figures and 3 with additional quali- tative results available in Appendix PP and the supplementary materials. The design of Animate-X is focused on single-character scenarios, significantly limiting its applicability to multi-character contexts. Though methods such as MimicMotion, MusePose, StableAnimator, and Human Vid can be extended to multi-human settings, they fail to generalize well across diverse humanoid forms. UniAnimate- and RealisDanceeDiiT exhibit only limited generalization and struggle handle occlusions (Figure 3 top and bottom rows) and identity confusion (Figure []] top row) in multi- humanoid scenarios. contrast, our approach demonstrates clear superiority in multi-humanoid settings, effectively addressing dense interactions, frequent occlusions, and diverse humanoid forms. Further results also highlight (1) its competitive performance in real-world scenarios (Figure I sec- ond row, Figure 3 top row, and Appendix), and (2) its robust generalization to settings involving more than two characters (Figure [ bottom row and Appendix). Both qualitative and quantitative evaluations consistently demonstrate the effectiveness of our unified motion representations and the
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Table I: Quantitative results the DualDynamics benchmark. Our method consistently outper- forms all basclines across all metrics, demonstrating significant advantages in multi-humanoii sce- narios involving diverse humanoid forms and rich interactions.
Video Metrics
Image Metrics
Method
FVD
FID-VID
FID
PSNR t
SSIM f
LPIPS
CLIP t
MimicMotion
312.4
74.23
71.72
0,6098
26.04
0.5165
0,4319
0.7842
MusePose
298.3
59.14
60.46
0.5741
28.12
0.5301
0.3416
0.8109
StamleAnimator
262.5
35.19
34.97
0.5261
27.11
0.5341
0.3721
0.7741
Animate-X
230.1
32.47
30.22
0.5361
28.15
0.5276
0,3780
0.8539
Human
174.6
29.12
31.58
0.5122
27.03
0.4576
0.4197
0.8214
UniAnimate-Dii
172.3
24.98
22.87
0,5743
29.11
0.5399
0,3482
0.8601
RealisDance-DiT
164.6
22.12
23.26
0.5341
29.06
0.5216
0.3271
0.8813
Ours
145.7
20.34
19.41
0.4836
29.19
0.5428
0.3213
0.9041
Table 2: Ablation study of the core components on the DualDynamics benchmark. The complete design achieves the best overall performance.
Video Metries
Image Metrics
Method
FVD
FID-VID
FID
L1
PSNR
SSIM
LPIPS
CLIP
Ours w/o MNP
198.5
27.28
25.29
0.5524
26.34
0.519
0.3751
0.7801
Ours w/o GAM
183.2
25.76
24.45
0.5343
27.98
0.537
0.3396
0,8941
Ours w/o DAA
167.1
21.80
22.03
0.5252
28.64
0.522
0.3452
0.8921
Ours w/o DCR
225.6
27.31
25.29
0.5541
26.28
0,511
0,3657
0,8270
Ours w/o H4S
174.3
24.22
21.46
0.5011
29.04
0.524
0,3429
0.8714
Ours
145.7
20.34
19.41
0.4836
29.19
0.5428
0.3213
0.9041
holistic 4D- ao-anchored paradigm in integrating these representations into the base diffusion model to drive character animation.
4.3 ABLATION STUDY
We conducted ablation study verify the necessity of our core components: Unified- Choreography Core, Hyper-Scene Integrator, and Hierarchical-4D Supervision. As shown in Ta- ble the results the DualDynnmics benchmark demonstrate the performance impact when these elements are individually altered or removed. Additionally, we provide qualitative visual experi- ments in Figure to visually illustrate the contribution of each module.
Generates framee hatch
Pose
Red charader
Ablation
Blue chanleter
MNP
GAM
wio DCR
TATS
1/T-05
(8)
(b)
Figure 4: Visual Comparison of Ablation Results. (a) The yellow and red meshes represent the target motions of the red and blue characters, respectively. The original design achieves the best visual performance among all variants. (b) Visualization of attention maps between frame latents and per-character unified motion representations.

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Shified-Choreographee Core. (1) Motion Normalization Pipeline (MNP). Directly converting raw SMPL parameters into joint coordinates and encoding them through linear layer degrades the ani- mation quality, especially when the reference character's 's physical attributes deviate from the motion source, leading to unrealistic limb configurations. (2) Group Attention Module (GAM). Removing the group attention module and directly injecting motion tokens into HSI leads to incorrect motion- character assignments, particularly when characters swap positions.
Hyper-Scene Integrator. (1) Depth-Aware Attention (DAA). Removing the of depth tokens and occlusion-loss supervision, the model could not correctly handle occlusions between characters. (2)
Dynamic C-RoPE (DCR). the absence of dynamic C-RoPE, we observe unstable training behavior and slower convergence, which ultimately causes marked deterioration in animation quality, (3) Furthermore, we visualize the attention maps between frame latents and per-character unified motion representations at t/TT 0.5. These visualizations clearly demonstrate that Hyper-Scene Integrator successfully captures the underlying 4D spatiotemporal structure, thereby enhancing the interactive fusion of video latents with the motion representations.
Hierarchical-4D Supervision. When the training objective restricted to fixed combination of MSE and occlusion loss, the model exhibits degraded control over humanoid poses, often resulting in blurry and temporally inconsistent motion renderings.
Scalability. Qualitative experiments in demonstrate that method can be generalized to sce- narios with more than two characters during the inference stage, which validates the superiority of holistic 4D-anchored paradigm. While the training phase incorporates sophisticated hierarchical supervision, the inference pipeline remains elegant and efficient: the UCC extracts unified motion representations, which then integrated by the HSI to provide precise, depth-aware guidance for video synthesis.
Target
Generated
Reference
Target
Generated
Figure 5: MotionWeaver can indeed process scenes with more than two humanoids.
CONCLUSION
We have presented Motion Weaver, holistic 4DDaachhred framework designed for multi-humanoid animation. By introducing the Unified-Choorogrranl Core (UCC), we explicitly bind identity- agnostic motion to character-speeific appearance features, effectively eliminating identity confu- sion during complex interactions. The Hyper-Scene Integrator (HSI) and Hierarchical-4D Supervi- sion (H4S) further unify these representations within a shared 4D space, providing the depth-aware guidance necessary to handle frequent occlusions. Supported by our MultiHuman46 dataset and DualDynamics benchmark, MotionWeaver achieves state-of-the-art performance and demonstrates strong generalization across diverse interaction patterns. Our work establishes a robust foundation for the holistic modeling of multi-character dynamics in video synthesis.
ACKNOWLEDGMENTS
This work was supported in part by NSFC under Grant 62192781, 62137002, Research Project Funded by the Key Research and Development Project in Shaanxi Province 2022GXLH-01-03, and the Project of China Knowledge Centre for Engineering Science and Technology.
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6 ETHICS STATEMENT
While our framework is intended purely for academic research, we recognize that its ability to syn- thesize controllable videos of real individuals could potentially be misused for generating deceptive or harmful content. We explicitly discourage such misuse and stress that applications must adhere ethical standards and legal regulations. Furthermore, our benchmark and methodology emphasize diversity in scenarios and humanoid forms to avoid bias toward specific identities. This research fully complies with the ICLR Code of Ethics.
REPRODUCIBILITY STATEMENT
We have made substantial efforts to ensure the reproducibility of our work. The overall model design thoroughly documented: Section 3.2 and Section 3.3 provide detailed descriptions of the model architecture, while Section 3.4 elaborates on the training strategies employed. Additional settings, including hyperparameters, optimization choices, and detailed implementation of the methods, presented in Section 4.1 and further expanded in the Appendix. To support reproducibility of the experimental pipeline, provide comprehensive description of the data collection process, pre- processing steps, and benchmark construction in Appendix | In addition, we release part of the constructed benchmark as well the key code components necessary reproduce the main periments in the supplementary materials. Together. these resources ensure that readers can both understand the methodological details and reliably reproduce our reported results.
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Wentao Zhu, Xiaoxuan Ma, Dongwoo Ro, Hai Ci, Jinlu Zhang, Jiaxin Shi, Feng Gao, Qi Tian, and Yizhou Wang. Human motion generation: A survey, 2023.
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Appendix
TABLE OF CONTENTS
Ablation on 4D-Anchored Framework
18
Standard Single-Person Animation Task
18
Evaluation on Real-Capture Sequences
18
D The Use of Large Language Models
18
Extraction of motion features
19
Extraction of appearance features
20
Reconstruction Operator
20
Rationale for Hyperparameter Selection H4S
21
MultiHuman446 Dataset and DualDynamics Benchmark
21
MultiHuman46 Dataset
21
1.2
DualDynamies Benchmark
22
Strategies for Memory- and Compute-Lfficient Training
23
Limitations
23
Broader Impacts
23
M User Study
24
More Details of Dynamic C-RoPE
24
Quantitative Evaluation Metrics
26
More Qualitative Results
27
Scene Descriptions for Image Generation Prompts
38
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A ABLATION ON 4D-ANCHORED FRAMEWORK
We selected the two strongest state-of-the-ar methods, UniAnimate-Dii and RealisDance-Dii for this comparative analysis. These models were subsequently fine-tuned on our specialized multi- human dataset, MultiHuman46. As demonstrated in [ these baselines remain unable robustly handle the core challenges inherent in multi-hummaoid animation, including diverse humanoid forms, complex interactions, and frequent occlusions, even after being trained task-specific data.
Reference
Target Pose
Motion (Ours)
UniAnimate Cocimatte--Taaaaae DiTirsstned
(Ideatity Confusion)
AniiAiimte-DB rretadd
(Occlusion Confusion)
ReallaDance-D/Taa Scavtoed
(Identity Confusion)
RealisDaano--D ai insiuned
(Poor Motion Grusp)
Figure 6: After fine-tumi tne-ttning baselines on our specialized multi-humas dataset, they still fail ro- bustly address the core challenges inherent to multi-humanoid animation.
B STANDARD SINGLE-PERSON ANIMATION TASK
We conducted qualitative experiments on the standard single-person TikToo benchmarks in 7 Our approach achieves competitive performance in standard single-person animation tasks, unequivo- cally demonstrating no performance degradation compared to state-of- ofthe-of-the-att specialized methods.
C EVALUATION ON
SEQUENCES
We present qualitative results We observe that our method still effectively suppresses identity drift even in this challenging scenarios, which further demonstrates the robustness of our approach. The experiments utilized 81 31-frame video sequences, corresponding to the maximum sequence length employed during the training of the base model.
D THE USE OF LARGE LANGUAGE MODELS
In preparing this paper, large language models (LLMs) were used only as general-purposs tools for improving the clarity and fluency of the writing. They did not contribute to research ideation, methodological design, data collection, or experimental analysis. All content was reviewed and revised by the authors, who take full responsibility for the final manuscript.
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Target Pose
MinicMotion
MusePose
Animate-X
MTVCrafter Crafter
UniAnimate
Rance DiTT
MotionWeave eaver
DiT
Figure 7: Qualitative experiments on TikTok datasets. Our approach is also capable of achieving competitive results in the single-human setting, which has been the focus of prior work.
wWW.
VOY
AVOY
AVOY
AVOY
Figure 8: Long-horizon real-capture sequence tests with frequent position exchanges.
E EXTRACTION OF MOTION FEATURES
We leverage the intermediate representations of the state-of-the-art multi-person pose estimation model CoMotion as our motion features. Specifically, CoMotion first employs an image backbone to encode the input image, followed by detection head that produces three groups of multi-scale feature maps with dimensions (1, (1.1024.16,16), (1.1024,8,8) and (1.1024,4,4) These feature maps then processed by three decoders, yielding total of 1344 candidate detections. A subsequent
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non-maximum suppression step is applied to filter redundant predictions and retain the final pose estimates. Owing to CoMotion's ss strong performance in robust pose estimation, we argue that its intermediate representations effectively capture motion-relevant information. We therefore adopt these intermediate features as motion signals to supervise the training of our model. However, since extracting motion features requires passing them through a VAE decoder, which introduces additional GPU memory overhead, we restrict the extraction to the first two latents of each input sequence.
F EXTRACTION OF APPEARANCE FEATURES
Our motivation originates from the observation in Wan2.1-2v-114-4488 that the features extracted from reference frames through the VAE encoder followed by the Patchifier play decisive role in guiding video generation. These features effectively encode the visual appearance of the reference characters. By isolating and reusing such features, we can explicitly associate each character with its corresponding motion signal. This binding is particularly crucial in multi-character scenarios, as enables the model to handle occlusions and inter-character interactions while preserving identity consistency.
In the original Wan2.1-12-114B-44 reference frame is first padded with blank frames to match the length of the target video sequence. The padded sequence then passed through the VAE encoder to obtain latent features. These features concatenated along the channel dimension with two additional components: (1) latent mask and (2) noise latents. The combined representation processed by the Patchifier to produce tokens that serve as inputs for the diffusion model.
workflow, the processing pipeline operates as follows. A
To remain consistent with the base model while ensuring that our extracted features align with its representational design, we propose the following procedure for extracting appearance features, as shown in Figure 9 We begin by masking out the character from the reference frame to construct new image. This image then treated as a single frame and passed through the VAE encoder to obtain the latent that captures fine-grained character appearance. We then duplicate the latent once approximate noise latents and construct an all-ones matrix surrogate for the latent mask. The three components, namely the latent, the duplicated "pseudo-noise" latent, and the all-ones mask, are concatenated along the channel dimension and passed through the Patchifier. The resulting output constitutes the appearance features, which explicitly encode character-specific visual cues while remaining aligned with the feature representation of the base model. This design creates the possibility of reliably binding motion signals to the correct identities, thereby enabling robust performance in complex multi-character video generation scenarios.
Opaddiing frapes
X
Wan-
Intent
Patchfier
video latents
N DiT Blocks
First frame
Encoder
mink
supy
Wan-
mask lalent
Patchfier
appearance
Group
Encoder
fealures
Figure 9: The upper figure illustrates the feature extraction pipeline of the Wan base model, while the lower figure presents our appearance feature extraction process, which is deliberately designed to align as closely as possible with the base model.
G RECONSTRUCTION OPERATOR
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Considering that SMPL is inherently human- centered model, the joint coordinates derived from SMPL parameters inevitably carry biases that are tightly coupled with human body proportions. Such biases may hinder the generalization of downstream motion representation, especially when extending animation to humanoid characters whose shapes, limb ratios, or stylistic designs deviate significantly from the human norm. To mitigate this limitation, we reconstruct a standardized skeleton by enforc- ing a fixed Euclidean distance between each pair of adjacent joints, thereby decoupling the structural representation from specific body proportions. This process results in uniform skeletal topology that emphasizes the relational geometry of joints rather than their absolute scales. By doing so, the result- ing skeleton provides a more robust and identity- agnostic foundation for motion encoding across di- verse humanoid characters. A visualization of our standardized skeleton is presented in Figure I0] where the normalized structure highlights consistent spatial relationships while eliminating distor- tions introduced by body-shape variability.
Figure 10: Visualization of our standardized skeleton.
H RATIONALE FOR HYPERPARAMETER SELECTION IN H4S
We conducted extensive qualitative experiments to investigate the behavior of Wan2.1-12V-1 480P during inference across different timesteps. The experiment was designed as follows: given specific timestep and its corresponding noisy latent, the model is tasked with estimating the velocity vector field. This estimated field, together with the noisy latent, is then used to directly compute the denoised latent, which is subsequently decoded back into the pixel space. A representative set of results is presented in Figure 11
Our observations confirm that the model exhibits the canonical characteristics of diffusion models: in the early denoising stages, the model focuses primarily capturing the global structure and layout of the scene, while in the later stages, it progressively refines fine-grained details. Notably, we find that around I/T 40%, Wan2. 1-12V-14B-4800 begins to predict almost accurate velocity field, producing relatively clear video frames and showing increasing attention to motion details. Based on this behavior, we empirically select 0.6 as the optimal setting.
I MULTIHUMAN46 DATASET AND DUALDYNAMIC BENCHMARK
1.1 MULTTHUMAN46 DATASET
We construct our MultiHuman46 dataset through a carefully designed multi-stage curation pipeline, which integrates heterogeneous data sources and applies rigorous filtering to guarantee both diver- sity and quality. The overall process comprises four stages: video collection, clip segmentation, automatic quality filtering, and manual validation.
Video collection. To maximize diversity in both appearance and motion, we draw upon three com- plementary sources of videos. First, a portion of the data comes from publicly available datasets, including Dataset (Jafarian & Park (021), Human VidDataset (Wang et al., (024b). Swing Dance (Maluleke et al., (2044), and CHI3D (Fieraru et al.] 2023), which are widely adopted for human motion research. Second, we collect web-crawled videos exclusively from publicly acces- sible materials, ensuring that all samples originate from open platforms. Third, to further broaden the diversity of scenarios, we incorporate Al-generated videos. Specifically, we first employ the FLUX.1 Krea model (Greenberg) 2025) generate still images depicting 2-3 humans standing in diverse scenes, with scene descriptions specified in prompts (see Section((). To ensure reliability, we use the CoMotion model to automatically verify that the number of generated humans matches the
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Figure 11: Qualitative visualization of Man2.1-12V-1444 14B-000PP across different denoising timesteps. The model captures the global layout in early stages and progressively refines fine details later stages. Around t/T 40%. it begins to predict nearly accurate velocity fields, producing clearer frames with increasing attention to detail.
prompt specification.. The validated images are subsequently converted into short dance sequences using Wan2. 10121-1-12-14B-48000 yielding synthetic yet realistic multi-human videos.
Clip segmentation. The collected videos are segmented into fixed-length clips of 49 frames. This process is guided by both the consistency of human counts and the confidence scores from the pose detector. Concretely, we traverse frames sequentially; as long the detected number of humans remains unchanged and the pose detector reports high confidence, we continue accumulating frames. Once either criterion fails, the current segment ends and new one begins from the following frame.
Quality-based filtering. To further ensure high-quality data, each clip is evaluated under four com- plementary metrics: (1) Aesthetic score predicted by the LAION-Aesthetics model (Schuhmann jet al. 2022), a CLIP-based linear estimator widely used to assess visual appeal; (2) Optical flow magnitude computed with UniMatch (Xu et al. 2023), which measures the degree of motion and fil- ters out overly static clips; (3) Laplacian blur score obtained via the Laplacian operator in OpenCV, which detects blurry frames and eliminates videos with poor sharpness; and (4) OCR text ratio pre- dicted by CRAFT (Back et al., 2019), which quantifies the proportion of textual regions and removes clips dominated by overlayed text. The thresholds for these four metries are 4.5, 2.5, 50, and 0.1, respectively. Any clip failing to meet any threshold is discarded.
Manual validation. To safeguard the reliability of the automated pipeline, we further conduct hu- man inspection. Specifically, we randomly sample 200 clips from the automatically curated pool and carefully examine them verify both the visual quality and the accuracy of the pose detec- tor. This step provides additional assurance that the dataset maintains high fidelity across multiple dimensions.
Through this pipeline, we ultimately construct dataset comprising 46 hours of multi- human video, spanning a diverse range of interaction patterns and scenes (e.g., boxing, fencing, and dancing), while rigorously adhering to strict quality standards.
I.2 DUALDYNAMICS BENCHMARK
Our benchmark is exclusively composed humanoid characters, specifically designed to evaluate model's capability in understanding motion dynamics and its robustness under diverse and chal-
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lenging conditions. To this end. collaborated with professional animation team curate high-quality benchmark. From their productions, we manually selected 300 video clips, each with a fixed length of 49 frames.
The benchmark encompasses a wide spectrum of humanoid forms and artistic styles, including pixe- lated stick figures, anthropomorphic animals, mechanical robots, anime characters, and 3D animated avatars. These characters are placed in richly interactive scenarios that involve frequent occlusions, overlaps, and intricate inter-character coordination. Such diversity not only tests whether model can generalize beyond standard human shapes, but also examines its ability to preserve motion con- trol and maintain character identity under heavy interaction.
By covering varied visual domains and challenging motion patterns, the benchmark provides a rig- orous testbed for assessing both the fidelity and robustness of nulti-humanoid animation models.
J STRATEGIES FOR MEMORY- AND COMPUTE- -FFICIEEEF TRAINING
To alleviate the memory and computational bottlenecks in training, we adopt several strategies. First, since the T5 text encoder consumes large amount of GPU memory, we pre-encode a set of generic prompts (e.g., "multiple humans performing interactive actions") into text embeddings before training. During training, we randomly sample from these pre-computed embeddings and inject them into the DiT model, which avoids repeatedly invoking T5 and thus significantly reduces memory usage while improving training efficiency. In addition, we enable gradient checkpointing to further lower memory overhead by trading off computation for reduced activation storage. When decoding video latents through the 3D VAF, although the runtime overhead is relatively small, pre- serving complex gradients introduces considerable memory cost; therefore, during training, we only decode the first two latents to extract the corresponding motion latents, effectively alleviating this issue. Finally, the overall training is conducted with the DeepSpeed framework using the ZeRO-2 optimization strategy, which ensures efficient memory utilization and stable scaling across multiple GPUs.
K LIMITATIONS
While Motion Weaver demonstrates strong performance across a wide range of scenarios, still exhibits several limitations, as illustrated in Figure [2
First, our method tends to generate blurry hands in real-world human scenes. This limitation arises from two main factors: (1) the Wan2.1-12V-144
base model itself exhibits weak generation capability for hands, and (2) our method does not explicitly control hand movements in real-world human scenarios. Unlike RealisDance (Zhou et al.) 2024), which leverages HaMeR provide de- tailed hand-level control signals, Motion Weaver does not incorporate such information during train- ing. This design choice is deliberate: our primary focus on multi-humanoid scenarios, where the hand structures of humanoid characters can differ significantly from those of humans. In such cases, enforcing hand-specific supervision would introduce a strong human-centric bias and ultimately compromise generalization to diverse humanoid forms. Second, similar to most diffusion based frameworks, Motion Weaver is computationally expensive and incurs non-trivial inference latency. This constraint poses challenges for real-time interactive applications, although it remains accept- able for offline content creation.
L BROADER IMPACTS
Motion Weaver enables pose-controllaale video generation of multiple humanoid characters by mod- eling in 4D space and learning identity-agnostic 4D motion representations. This capability unlocks new possibilities across a wide spectrum of applications, including creative content production, pro- fessional animation, immersive gaming, virtual reality/augmented reality experiences, digital avatars for social interaction, virtual cinematography, and human-robo robot interaction simulation. In parallel, we introduce DualDynamics, the first dedicated benchmark for multi-hun siltiiid image animation. DualDynamics is deliberately designed assess model's generalization ability at inference time,
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G
Generated
wilsard HOREERRREY
Generated
Figure 12: Motion Weaver tends produce blurry hands in real-world human scenes due to weak base-model hand generation and the absence of explicit hand-level control, a deliberate choice to avoid human-centric bias and preserve generalization across diverse humanoid forms.
thereby serving valuable resource for advancing research and benchmarking future methods in this emerging field.
Nevertheless, model also capable of producing realistic multi-human videos, which raises po- tential concerns about misuse. These risks include, but are not limited to, deepfake creation. identity impersonation, and privacy infringements. To mitigate such risks, we plan release public model checkpoints with visible watermarks, and to accompany the release of code and models with clear ethical guidelines and explicit usage licenses. Under these safeguards, we believe Motion Weaver can serve responsible tool that not only pushes forward scientific research but also fosters inno- vation creative industries.
M USER STUDY
To gain an accurate understanding of user preferences, we conducted a comprehensive survey as- sessing five key perceptual dimensions: Visual Quality, Motion Alignment, Interaction Coherence, Occlusion Handling, and Character Preservation. We compared our proposed framework, Motion- Weaver, against two competitive baselines, RealisDance-DiT and UniAnimate-DiT. The results, shown in Figure [3 illustrate the proportion of total votes received by each method. Across all five evaluation dimensions, Motion Weaver consistently secures the largest share of user preference. These findings underscore the strength of our framework in balancing both identity preservation and motion fidelity, thereby demonstrating its effectiveness aligning with human perceptual expecta- tions for nulti-humanoid animation.
N MORE DETAILS OF DYNAMIC C-ROPE
The HSI module is designed to establish a unified 4D space between video latents and motion tokens. It consists of two complementary components: (1) depth-aware attention mechanism that implicitly encodes the z-axis (depth), and (2) Dynamic C-RoPE, which explicitly models the remaining temporal and spatial axes (1, v).
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Visual Quality
38%
33%
29%
Motion A Alignment
46%
28%
26%
Interaction Coherence
42%
32%
26%
Occlusion Handling
68%
21%
11%
Character Preservation
49%
24%
27%
Overall Satisfaction
49%
28%
23%
Figure 13: User preferences across visual quality, motion alignment, interaction coherence, occlu- sion handling, and character preservation for different methods.
Unlike conventional RoPE, which primarily encodes relative positional relationships self- attention layers, Dynamic C-RoPE incorporated into cross-attention explicitly capture posi- tional dependencies between queries and keys across different modalities. Inspired by Wan2.1-12V- 14B-480P, we divide each query and key embedding into three subspaces within the multi-head cross-attention mechanism. Each subspace is rotated using the corresponding rotary matrices, en- abling simultaneous encoding of temporal and spatial relations.
A critical challenge in applying RoPE to cross-attention is the aligmment of queries and keys within shared coordinate system. Specifically, query positions (æ. y) are naturally defined in the image coordinate system, whereas key positions (xr, y) are defined in the camera coordinate system. To bridge this discrepancy, we leverage the depth value 2 of each key to project its (ar, y) coordinates onto the image plane. Consequently, identical (t, z, values from queries and keys correspond to the same spatiotemporal location, ensuring consistent positional alignment across modalities.
In our setting, queries with the same index in the attention computation are associated with fixed rotary matrix, making them static. By contrast, keys with the same index may map to different positions due to varying motion dynamics, rendering them inherently dynamic. To account for this discrepancy, we dynamically select the appropriate rotary matrix for each key based on its positional information before applying the rotation. This ensures that the encoded representations accurately reflect the relative spatiotemporal structure.
To further clarify this process, we provide code that illustrates how the Dynamic C-RoPE procedure applies dynamic rotation to keys:
Listing 1: Illustration of Dynamic C-RoPE procedure: dynamic rotation of keys def Dynamic.C.RoPP..ker (x, grid_sizes freqs, motion_pos) ): b. n. с = x. size (0), x. size (2), x. size (3) // 2
ps motion motionnpps..... size(1 (1)
ps.token = x. size (1)
token_len fs_tooen__een / //ps
freqs freqs. split ([c output []
(c 11 3), 11 3, 11 3], dim=l)
for i. (f, h, w) in enumerate ( grid sizes. tolist O)):
## Rotary matrix candidate pool
freqs_i_fxy = torch. cat ([
freqs [O][: view (f, 1, 1, exp.nx (f, h, w, -1),
freqs [1][:h]. view (1, h, 1. expanx (f, h. w. -1),
freqs [[][:]]]]]]]]]]]]]]]]]]]]]]]]]]] view (1, 1, w. -1). (f, h w, -1)
].
dim=-1) reshape (f, h, w, 1, -1)
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x_i torch.
(x[i]. to (oo(toog (torch. float32
). reshape (ps, token_len, n, -1, 2))
i motion pos[i]
scales = torch. tensor ([w-1, h-1, 0], device=m device
view 1,
m_i.scaled = (m-i scales). long ()
Lidx torch arange device=m.i. device
view(1, expand (ps, f)
h_idx m.i.scaled 1]
w_idx m_i_scaledd.... 0]
## dynamiclly select
freqs_fxy.selecter d2, ..d2.dd. d3. d4 =
fregs_i.fxy(t.ids h_idx, w_idx]
shape
freqs selected freqs fxy selected .ussuuceeu unsqueeze (2
).
d3224,4,, d4)
## rotate the keys x_i torch,
(x.i flatten (3)
d1,d2,d3 d4=x-i shape x_i x.i reshape (dI=d2,d) d3 d4)
output. append i) return torch. stack (oupput). float ()
O QUANTITATIVE EVALUATION METRICS
The detailed formulations of the evaluation metrics are presented below.
FVD L:
We evaluate temporal realism using features from the Inflated 3D ConvNet (I3D). Each video is partitioned into non-overlapping 16-frame segments, and embeddings are extracted. The em- pirical means Mr, Hy and covariances E,, Σ, are computed for real and generated sets, and the distance is
FVD
||,, 1rl12 + THE, + E,, - -2)
(11)
FVD-VID J:
Instead of treating short clips independently, we first average all I3D embeddings belonging to the same video, resulting in one descriptor per sequence. Statistics Pr, (tf, Σ,, Ey are then estimated across the video-level features, and the Fréchet distance calculated as
= ||ue 15113 TrEE, E, 2/E,
(12)
FID J:
For each individual frame, we extract activations from a pretrained Inception-V3. V3 network. By aggregating these embeddings into Gaussian distributions (M,, Ex, and (Hz Ey), define
FID = ||и, - 2rl1 + TY + E, - 2(E,E)),
(13)
L1 4:
Given a ground-truth video I, and a generated sequence i, both of size H x C, the pixel-wise reconstruction error
Ll 255 THWC ΣΣΣ ((x,v.) L(x,8,0)..
(14)
PSNR T:
We first compute the mean squared error for each frame:
MSE
1
ΣΣΣ
(15)
HWC
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Peak hignal-to-oiise ratio is then defined
PSNR 20 logio
253 VMSE
(16)
SSIM f:
For each frame t and color channel c, measure structural similarity:
SSIM; (S(s()e(ss..))) Te(,,))).
(17)
The reported score is the average across all frames and channels:
SSIM
SSIM,
(18)
TC
LPIPS L:
For each video frame, let (be()) be the feature map from the L-th layer of a pretrained backbone, and let wi denote the learned channel-wise weights. The frame-level perceptual distance is defined as
Σ 4. = T. 1=1
we o (de(T) te ( (1)
(19)
H.W,
The video-level LPIPS score is then obtained by averaging across all frames:
LPIPS
T Σ 4.
(20)
CLIP1:
At each frame 1, both the ground-truth and generated images are embedded with the CLIP image encoder, yielding Ve, 0g Rd. After lg normalization, the similarity is measured by cosine distance:
8t
(21)
The final CLIP score for the video is computed as the temporal average:
1
CLIP
St-
(22)
P MORE QUALITATIVE RESULTS
We present detailed qualitative comparisons on the DualDynamics benchmark dataset in Figures
14
15
and 16
Additional qualitative results on out-of oat-of-benchmark, data are shown in Figures 17
18
19
on the benchmark dataset are provided Figures| 21 and 22 while more qualitative inference results on out-of-benchhark data are also
and 20
Furthermore, extended results of
included in Figure 23 For traeeby--raned comparisons, we refer readers to the supplementary
videos.
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Figure 14: Qualitative comparisons the DualDynamies benchmark dataset. From top to bottom and left to right: benchmark video (ground truth), target motion, MinicMotion, MusePose, Sta- bleAnimator, Animate-X, Human Vid, UniAnimate-Diiz RealisDance-Diis and our Motion
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OPEL JOWKL
Figure 15: Qualitative comparisons on the DualDynamics benchmark dataset. From top to bottom and left to right: benchmark video (ground truth), target motion, MinicMotion, MusePose, Sta- bleAnimator, Animate-X, Human Vid, UniAnimate-Dii. RealisDance--itt and our Motion Weaver.
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Figure 16: Qualitative comparisons on the DualDynamics benchmark dataset. From top to bottom and left to right: benchmark video (ground truth), target motion, MimicMotion, MusePose, Sta- Alimno,,,,r, Animate-X, Human Vid, UniAnimat--DDii RealisDance-Ditt DIT, and our Motion Weaver.
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Figure 17: Qualitative results. From top to bottom and left to right: reference image, target motion, MinicMotion, MusePose, Atimto,n AnimateX, Human Vid, UniAnimate-Di DłT, RealisDance- DIT, and our Motion Weaver.
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Figure 18: Qualitative results. From top to bottom and left to right: reference image, target motion, MimicMotion. MusePose, StableAnimator,, AnimateX, Human UniAnimate- -DT. RealisDance- DIT, and our Motion Weaver.
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abe
ane
Figure 19: Qualitative results. From top to bottom and left to right: reference image, target motion, MimicMotion. MusePose, StamleAnimatorr, AnimateX, Human Vid, UniAnimate-Diin RealisDance- DiT, and our Motion Weaver.
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55
AMF
Uitaln O1
Figure 20: Qualitative results. From top to bottom and left to right: reference image, target motion, MimicMotion. MusePose, StamleAnimator, AnimateX, Human Vid, UniAnimate- -iI,, RealisDance- DIT, and our Motion Weaver.
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Benchmark
Motion
Generated
Figure 21: Qualitative results of Mevver on the DualDynamics benchmark dataset.
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Benchmark
Motion
Generated
Figure 22: Qualitative results of Meaver on the DualDynamics benchmark dataset.
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Reference Image
Motion
Generated
Motion
Generated
Figure 23: Qualitative results of Motion Weaver.
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Q SCENE DESCRIPTIONS FOR IMAGE GENERATION PROMPTS
The scene descriptions used in the prompts for image generation are provided here, offering a con- cise overview of the input conditions. For more details how these prompts are utilized in our framework, please refer to Section
in a dense tropical rainforest
on the snowy peaks of the Himalayas
in a canyon carved by river
beside tranquil bamboo grove
frozen lake under the moonlight
in a valley filled with morning mist
by a hot spring in the mountains on the shoreline with crashing waves in a meadow full of fireflies night on winding mountain trail in jungle with ancient ruins
golden desert dune sunset
beside river with drifting lotus leaves
in a field of colorful tulips
on cliffs facing the endless sea
in a forest with red and golden autumn leaves
by glacier with floating icebergs
in canyon glowing with sunset light
hillside covered with tea plantations
in a peach orchard during full bloom
by a secluded waterfall hidden in the jungle
in a pine forest after snowfall
on a plateau with yaks grazing
beside calm fjord surrounded by mountains
in sunflower field stretching the horizon
volcanic island with black sand beaches in a lavender field under the evening sky in a cherry blossom park during spring beside a crystal-clear stream with pebbles
in a meadow with rainbow after rain on prairie with galloping wild horses
in canyon with turquoise river
coral beach with seashells
under giant sequoia trees
in a field covered with morning dew
floating on clouds above the world
inside an ancient library filled with scrolls
in a desert oasis with palm trees bridge made of glowing light
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in a realm of floating islands
inside crystal ice cave
in a volcanic landscape with flowing lava
on the surface of the moon
in a garden of giant mushrooms inside glowing underwater city
inside traditional Japanese teahouse
in a medieval European castle courtyard
on a quiet cobblestone street
on a rooftop overlooking skyscrapers
in a cozy countryside farmhouse
at festive lantern festival
in a futuristic cyberpunk alley
inside a grand opera house
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Reviews:
[{"review_id": "GHKNw7Zz94", "reviewer": "Reviewer_Yd3q", "summary": "This paper proposes MotionWeaver, a multi-humanoid image-to-video framework that (1) learns identity-agnostic 4D motion tokens via the Unified-Choreography Core (UCC), (2) fuses motion and video latents in a shared 4D space using the Hyper-Scene Integrator (HSI) with depth-aware attention and Dynamic Cross-RoPE, and (3) trains with Hierarchical-4D Supervision (H4S) that adds occlusion supervision at high noise and motion-level supervision at low noise. The authors also curate MultiHuman46 (46 hours of multi-human video) and release DualDynamics (300 two-character clips) to stress-test interactions/occlusions, reporting consistent SOTA on that benchmark.", "questions": "Please see the weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper clearly argues that 2D control entangles appearance and motion and lacks explicit depth reasoning in multi-person scenes, and responds with a fully 4D-anchored pipeline (UCC, HSI and H4S) that separates motion from morphology, injects depth ordering, and supervises motion explicitly.\n\n2. UCC standardizes SMPL joints to strip appearance cues and binds per-person motion with group attention. HSI adds depth-aware cross-attention plus Dynamic Cross-RoPE to align (t, x, y) between camera and image planes. H4S schedules occlusion vs. motion-level supervision by noise step. These pieces are concrete and novel in combination.\n\n3. This paper provides extensive experiments to support its claim. On DualDynamics, MotionWeaver tops all baselines on every metric, supporting the claim that explicit depth/occlusion handling and identity-motion binding help in multi-humanoid settings. Module-wise ablations and attention visualizations show each component (motion normalization, group attention, depth-aware attention, Dynamic Cross-RoPE, timestep-aware training) matters. Qualitative figures highlight fewer identity swaps and cleaner occlusion ordering than baselines.", "weaknesses": "My major concern about this paper is the detailed comparison over existing methods to show the contribution clearly. The multi-person interaction and 4D motion tokens are adopted by previous methods already. I hope the authors could clearly clarify the difference with existing methods.\n\n1. MTVCrafter (Ding et al., 2025) also models raw 4D motion via a tokenizer (4DMoT) and conditions a DiT with 4D positional encodings, reporting large gains on open-world human animation. MotionWeaver likewise trains a 4D tokenizer, uses 4D positional cues (Dynamic Cross-RoPE), and claims generalization beyond humans, so the novelty boundary feels blurred. The paper would benefit from a direct experimental comparison (same control signals/base model), or at least an ablation contrasting quantized motion tokens (MTVCrafter) versus the authors’ standardized-skeleton motion units and group-attention binding.\n\n2. DanceTogether (Chen et al., 2025) introduces a MaskPoseAdapter that fuses tracking masks with noisy pose heatmaps at every denoising step to suppress identity drift when actors swap positions and interact over long horizons. MotionWeaver’s identity binding relies on group attention and occlusion-aware depth cues but evaluates on 49-frame clips. It doesn’t stress identity under frequent cross-overs or long sequences. A head-to-head on DanceTogether’s scenarios/metrics, or adding long-horizon tests with frequent position exchanges, would strengthen claims. Besides, Structural Video Diffusion [a] in ICCV 2025 proposes identity-specific embeddings plus structural learning with depth + surface normals. MotionWeaver also models depth (via depth-aware attention and occlusion loss). A comparison on the [a]'s setup or cross-evaluation across datasets would clarify when explicit identity embeddings vs. identity-agnostic binding are preferable.\n\n[a] Zhenzhi Wang, Yixuan Li, Yanhong Zeng, Yuwei Guo, Dahua Lin, Tianfan Xue, Bo Dai. Multi-identity Human Image Animation with Structural Video Diffusion, ICCV 2025. https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Multi-identity_Human_Image_Animation_with_Structural_Video_Diffusion_ICCV_2025_paper.pdf\n\n3. DualDynamics emphasizes two-character, 49-frame interactions crafted by an animation team. MultiHuman46 includes AI-generated material and web-crawled clips. While appropriate for controlled studies, this may underestimate long-range identity drift and real-world messiness. Evaluating on longer, real-capture sequences (or adopting external multi-human sets) would improve external validity.", "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": "h5cl5cQsMR", "reviewer": "Reviewer_FnGY", "summary": "The paper presented MotionWeaver which synthesizes multi-character video given the reference character images and pose sequences. The proposed MotionWeaver includes these modules:\n\n1)\tUnified-Choreopgraphy Core (UCC), which extracts 4D motion representations of characters that exclude their appearances. UCC uses SMPL to represent 3D human body as their joint coordinates and normalizes to a standardized skeleton. Individual character is segmented from the reference image to bind with the motion representations by a pre-trained I2V model.\n\n2)\tHyper-Scene Integrator (HIS), which estimates the depth from the occlusion-loss supervision and encodes with a dynamic C-RoPE. This representation allows a Hierachical-4DSupervision (H4S) to train the model with occlusion loss and motion-level loss, etc. \n\nThe MotionWeaver model is trained on a new 46-hour multi-human video dataset and evaluated on a new DualDynamics benchmark including 300 videos with complicated interactions between two humanoid characters. MotionWeaver outperforms 7 recent character animation models on this new DualDynamics benchmark.", "questions": "Throughout the paper, it seems all approaches and the training/benchmark datasets focused on two-humanoid interactions. So perhaps “two humanoid image animation” is a more proper claim than “multi-humanoid image animation”?\n\nll.251: “and augment them with the latent timestep t”, could you elaborate how to do this?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "This work is well motivated. Character video generation including two characters remains very challenging. The paper presented a clear analysis of the issues of motion representation and 4D modeling. \n\nThe proposed technical approaches, i.e., UCC, HIS, H4S, are technical novel and make sense to improve the performance of multi-character video generation.\n\nThe experiments on the new DualDynamics benchmark show clear improvement, which outperform recent related works substantially.", "weaknesses": "The paper only reported the performance comparison on the new DualDynamics benchmark. Why only part of the constructed benchmark will be released? Then how do subsequent works compare with MotionWeaver?\n\nAll the technical approaches, i.e., UCC, HIS, H4S, are backward applicable to the single character case. So please show the performance of MotionWeaver on Fashion and TikTok, thus, the readers have a clear understanding of the advantages of these modules.", "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": "PIDkAESx4h", "reviewer": "Reviewer_9egx", "summary": "The paper proposes MotionWeaver for multi-humanoid image animation. MotionWeaver introduces unified motion representations to extract identity-agnostic 4D motions and bind them to corresponding characters, enabling generalization across diverse humanoid forms. It fuses motion representations with video latents to control the generated motion. The paper also introduces training datasets and benchmarks to support this task. Results show the effectiveness of the proposed method.", "questions": "1. What is the training settings of the comparing methods? Are they trained on the same data? Could the improvements come from different training data?\n2. What is the inference time comparison to previous methods? How would the error propagation affect the final results?\n3. What is performance of the proposed method on existing benchmarks?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The multi-humanoid image animation is an important, practical and interesting task.\n2. The proposed method designs several components to extract motion to improve generalization, which is well motivated and reasonable.\n3. The results show that the proposed method outperforms previous methods.", "weaknesses": "1. The training setting of comparing methods seems to be different from the proposed method. Are the comparing methods trained on the same data?\n2. The proposed method consists several steps. What is the inference time comparison to previous methods? How would the error propagation affect the final results?\n3. The evaluation is only conducted on the proposed benchmark. It is unclear whether the proposed method outperforms comparing methods on existing benchmarks.", "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": "1CW8tTjPES", "reviewer": "Reviewer_dGPY", "summary": "This paper introduces Motion Weaver, a novel and holistic 4D-Anchored Framework designed for multi-humanoid image animation. It specifically tackles the challenges of inter-character occlusion, complex interactions, and diverse humanoid forms—issues that often cause existing single-person animation models to fail. The method utilizes a Unified Choreography Core (UCC), a Hyper-Scene Integrator (HSI), and a Hierarchical 4D Supervision (H4S) strategy, alongside contributing the MultiHuman46 dataset and DualDynamics benchmark. The results are qualitatively impressive in multi-character settings.", "questions": "Please refer to Weaknesses for more details. If my concerns are solved, I am glad to raise my score.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThe paper tackles the crucial and challenging problem of multi-character image animation, which current state-of-the-art methods typically cannot handle robustly due to their focus on single-person scenarios.\n2.\tThe introduction of the MultiHuman46 dataset and the DualDynamics benchmark provides essential resources for advancing research in the multi-humanoid animation field.", "weaknesses": "1.\tThe proposed Motion Weaver is trained on the dedicated, multi-humanoid focused MultiHuman46 dataset. The methods chosen for comparison (e.g., RealisDance-DiT, UniAnimate-DiT, etc.) are predominantly designed and trained on single-person or general video datasets. Comparing Motion Weaver (trained on multi-person data) against baselines (trained on single-person data) on a multi-person benchmark (DualDynamics) creates a massive advantage for the proposed method. The baseline models are being tested significantly Out-of-Distribution (OOD) for the task of multi-person interaction and occlusion handling. The reported performance gain is likely inflated due to this training data mismatch.\n2.\tThe authors must perform a fair comparison by either: a) Re-training/Fine-tuning all comparable baselines on the MultiHuman46 training set and reporting the results. This would isolate the performance difference due to the architectural design. b) Alternatively, the authors should present an ablation study where Motion Weaver is only trained on a standard, single-person dataset and then tested on the multi-person benchmark, to demonstrate the robustness of its 4D framework even without specific multi-person training data.\n3.\tThe paper focuses almost exclusively on multi-humanoid scenarios. It is critical to demonstrate that the novel 4D-anchored components and mechanisms designed for multiple characters do not degrade performance or introduce unnecessary complexity/overhead when applied to standard, single-person animation tasks.", "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)