AnimaX: Animating the Inanimate in 3D with Joint Video-Pose Diffusion
  Models
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. Project page:
https://anima-x.github.io/{https://anima-x.github.io/}.
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Zehuan Huang
admin_assigned
2025-06-25T08:19:21.031Z
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zehuan-huang
user
huanngzh
685b5a46d2ee4fac76521dcf
Haoran Feng
claimed_verified
2025-06-25T09:19:31.409Z
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Haoran Feng
user
fenghora
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Yangtian Sun
claimed_verified
2025-06-25T09:19:29.490Z
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Yangtian Sun
user
Yang-Tian
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Yuanchen Guo
685b5a46d2ee4fac76521dd2
Yanpei Cao
admin_assigned
2025-06-25T08:21:27.467Z
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Yanpei Cao
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pookiefoof
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Lu Sheng
claimed_verified
2025-06-25T09:19:33.186Z
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/avatars/65f20601ef9b8ebfdddadd737f9153d6.svg
Lu Sheng
user
lsheng2024
AnimaX: Animating the Inanimate in 3D with Joint Video-Pose Diffusion
  Models
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. Project page:
https://anima-x.github.io/{https://anima-x.github.io/}.
AnimaX creates multi-skeleton 3D animations by blending video diffusion model priors with skeleton-based control, using joint video-pose diffusion and shared positional encodings.
feed-forward 3D animation framework video diffusion models skeleton-based animation motion synthesis high-dimensional deformation spaces 2D pose maps joint video-pose diffusion template renderings textual motion prompt shared positional encodings modality-aware embeddings spatial-temporal alignment inverse kinematics VBench category-agnostic 3D animation