MagicArticulate: Make Your 3D Models Articulation-Ready
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.
67b4028237db78705fb256e1
Chaoyue Song
claimed_verified
2025-02-18T09:31:40.771Z
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/avatars/6c8e4a66e1b8b3c786a4000210089392.svg
Chaoyue Song
user
chaoyue7
67b4028237db78705fb256e2
Jianfeng Zhang
67b4028237db78705fb256e3
Xiu Li
67b4028237db78705fb256e4
Fan Yang
67b4028237db78705fb256e5
Yiwen Chen
67b4028237db78705fb256e6
Zhongcong Xu
67b4028237db78705fb256e7
Jun Hao Liew
67b4028237db78705fb256e8
Xiaoyang Guo
67b4028237db78705fb256e9
Fayao Liu
67b4028237db78705fb256ea
Jiashi Feng
67b4028237db78705fb256eb
Guosheng Lin
MagicArticulate: Make Your 3D Models Articulation-Ready
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.
MagicArticulate automatically converts static 3D models into articulation-ready assets using a large-scale benchmark and innovative methods for skeleton generation and skinning weight prediction.
skeleton generation auto-regressive transformer sequence modeling functional diffusion process volumetric geodesic distance priors skinning weights