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Cited by 3Human Motion and Animation3D Shape Modeling and AnalysisGenerative Adversarial Networks and Image Synthesis

AnimaX: Animating the Inanimate in 3D with Joint Video-Pose Diffusion Models

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Huang, Zehuan, Feng, Haoran, Sun, Yang-Tian, Guo, Yuan-Chen, Cao, Yan-Pei, Sheng, Lu·2025·Proceedings of the SIGGRAPH Asia 2025 Conference Papers
DOI 10.1145/3757377.3763885
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Abstract

We present AnimaX, a feed-forward 3D animation framework that bridges the motion priors of video diffusion models with the controllable structure of skeleton-based animation. Traditional motion synthesis methods are either restricted to fixed skeletal topologies or require costly optimization in high-dimensional deformation spaces. In contrast, AnimaX effectively transfers video-based motion knowledge to the 3D domain, supporting diverse articulated meshes with arbitrary skeletons. Our method represents 3D motion as multi-view, multi-frame 2D pose maps, and enables joint video-pose diffusion conditioned on template renderings and a textual motion prompt. We introduce shared positional encodings and modality-aware embeddings to ensure spatial-temporal alignment between video and pose sequences, effectively transferring video priors to motion generation task. The resulting multi-view pose sequences are triangulated into 3D joint positions and converted into mesh animation via inverse kinematics. Trained on a newly curated dataset of 160,000 rigged sequences, AnimaX achieves state-of-the-art results on VBench in generalization, motion fidelity, and efficiency, offering a scalable solution for category-agnostic 3D animation.

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  • AnimaX, AnimaMimic, Make-It-Poseable, and AnimateAnyMesh animate existing meshes or generate motion-conditioned deformations, but do not primarily focus on jointly generating a new mesh, skeleton, and skinning weights as a complete rigged asset.

    “AnimaX Huang et al. [2025], AnimaMimic Xie et al. [2025], Make-It-Poseable Guo et al. [2025c], and AnimateAnyMesh Wu et al. [2025] animate existing meshes or generate motion-conditioned deformations, but do not primarily focus on jointly generating a new mesh, skeleton, and skinning weights as a complete rigged asset.”

    From Rigel3D: Rig-aware Latents for Animation-Ready 3D Asset Generation (2026) · cites this paper as Huang et al. [2025]

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Cited by

Sorted by citations

Authors
Huang, Z., Feng, H., Sun, Y.-T., Guo, Y.-C., Cao, Y.-P., & Sheng, L.
Affiliations
  • Beihang University (CN)
  • Tsinghua University (CN)
  • University of Hong Kong (HK)
  • VAST, Beijing, China
ORCID
Venue
Proceedings of the SIGGRAPH Asia 2025 Conference Papers
Publisher
ACM
Published
2025-12-14
Pages
1-13
DOI
10.1145/3757377.3763885
OpenAlex
W4417125358
Cited by
3
References
9
Citations by year
2026: 3
FWCI
6.56
Type
conference paper
Language
en
Open access
Locations
  • SIGGRAPH Asia Conference Papers (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Funders
National Natural Science Foundation of China (62132001)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Human Motion and Animation
  • Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
Keywords
3D animation, video diffusion models, skeleton-based animation, inverse kinematics, positional encoding, motion synthesis, text-to-motion

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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