title: MagicArticulate: Make Your 3D Models Articulation-Ready
summary: 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.
paper_authors._id: 67b4028237db78705fb256e1
paper_authors.name: Chaoyue Song
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-18T09:31:40.771Z
paper_authors._id: 64fb31a34c8924c4fe7498bc
paper_authors.avatarUrl: /avatars/6c8e4a66e1b8b3c786a4000210089392.svg
paper_authors.fullname: Chaoyue Song
paper_authors.type: user
paper_authors.user: chaoyue7
paper_authors._id: 67b4028237db78705fb256e2
paper_authors.name: Jianfeng Zhang
paper_authors._id: 67b4028237db78705fb256e3
paper_authors.name: Xiu Li
paper_authors._id: 67b4028237db78705fb256e4
paper_authors.name: Fan Yang
paper_authors._id: 67b4028237db78705fb256e5
paper_authors.name: Yiwen Chen
paper_authors._id: 67b4028237db78705fb256e6
paper_authors.name: Zhongcong Xu
paper_authors._id: 67b4028237db78705fb256e7
paper_authors.name: Jun Hao Liew
paper_authors._id: 67b4028237db78705fb256e8
paper_authors.name: Xiaoyang Guo
paper_authors._id: 67b4028237db78705fb256e9
paper_authors.name: Fayao Liu
paper_authors._id: 67b4028237db78705fb256ea
paper_authors.name: Jiashi Feng
paper_authors._id: 67b4028237db78705fb256eb
paper_authors.name: Guosheng Lin
paper_title: MagicArticulate: Make Your 3D Models Articulation-Ready
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: skeleton generation auto-regressive transformer sequence modeling functional diffusion process volumetric geodesic distance priors skinning weights