MagicArticulate: Make Your 3D Models Articulation-Ready
Authors: name: Chaoyue Song; name: Jianfeng Zhang; name: Xiu Li; name: Fan Yang; name: Yiwen Chen; name: Zhongcong Xu; name: Jun Hao Liew; name: Xiaoyang Guo; name: Fayao Liu; name: Jiashi Feng; name: Guosheng Lin
Published: 2025-02-17 18:53:27

Abstract: With the explosive growth of 3D content creation, there is an increasing
demand for automatically converting static 3D models into articulation-ready
versions that support realistic animation. Traditional approaches rely heavily
on manual annotation, which is both time-consuming and labor-intensive.
Moreover, the lack of large-scale benchmarks has hindered the development of
learning-based solutions. In this work, we present MagicArticulate, an
effective framework that automatically transforms static 3D models into
articulation-ready assets. Our key contributions are threefold. First, we
introduce Articulation-XL, a large-scale benchmark containing over 33k 3D
models with high-quality articulation annotations, carefully curated from
Objaverse-XL. Second, we propose a novel skeleton generation method that
formulates the task as a sequence modeling problem, leveraging an
auto-regressive transformer to naturally handle varying numbers of bones or
joints within skeletons and their inherent dependencies across different 3D
models. Third, we predict skinning weights using a functional diffusion process
that incorporates volumetric geodesic distance priors between vertices and
joints. Extensive experiments demonstrate that MagicArticulate significantly
outperforms existing methods across diverse object categories, achieving
high-quality articulation that enables realistic animation. Project page:
https://chaoyuesong.github.io/MagicArticulate.