AnimaX: Animating the Inanimate in 3D with Joint Video-Pose Diffusion
  Models
Authors: name: Zehuan Huang, name: Haoran Feng, name: Yangtian Sun, name: Yuanchen Guo, name: Yanpei Cao, name: Lu Sheng

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/}.