title: Building Interactable Replicas of Complex Articulated Objects via
    Gaussian Splatting
summary: Building articulated objects is a key challenge in computer vision. Existing
  methods often fail to effectively integrate information across different object
  states, limiting the accuracy of part-mesh reconstruction and part dynamics
  modeling, particularly for complex multi-part articulated objects. We introduce
  ArtGS, a novel approach that leverages 3D Gaussians as a flexible and efficient
  representation to address these issues. Our method incorporates canonical
  Gaussians with coarse-to-fine initialization and updates for aligning
  articulated part information across different object states, and employs a
  skinning-inspired part dynamics modeling module to improve both part-mesh
  reconstruction and articulation learning. Extensive experiments on both
  synthetic and real-world datasets, including a new benchmark for complex
  multi-part objects, demonstrate that ArtGS achieves state-of-the-art
  performance in joint parameter estimation and part mesh reconstruction. Our
  approach significantly improves reconstruction quality and efficiency,
  especially for multi-part articulated objects. Additionally, we provide
  comprehensive analyses of our design choices, validating the effectiveness of
  each component to highlight potential areas for future improvement.
paper_authors._id: 67c185f46a31b8fe77434551
paper_authors.name: Yu Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-28T12:35:40.098Z
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paper_authors.avatarUrl: /avatars/3e281e547e1697e1c06805e7e63f3918.svg
paper_authors.fullname: Yu Liu
paper_authors.type: user
paper_authors.user: YuLiu
paper_authors._id: 67c185f46a31b8fe77434552
paper_authors.name: Baoxiong Jia
paper_authors.status: admin_assigned
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paper_authors._id: 6304b389bad6ce7fc02691d5
paper_authors.avatarUrl: /avatars/a762ca59624ce409650165f36b973488.svg
paper_authors.fullname: Baoxiong Jia
paper_authors.type: user
paper_authors.user: BuzzBeater
paper_authors._id: 67c185f46a31b8fe77434553
paper_authors.name: Ruijie Lu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-28T12:35:27.394Z
paper_authors._id: 64ab8cb76324705e6a65f7c4
paper_authors.avatarUrl: /avatars/15dcae6c345d31ea6e17c11108a7deb7.svg
paper_authors.fullname: Ruijie Lu
paper_authors.type: user
paper_authors.user: JasonAplp
paper_authors._id: 67c185f46a31b8fe77434554
paper_authors.name: Junfeng Ni
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-28T12:35:20.550Z
paper_authors._id: 65ae5edddacd99fd58277620
paper_authors.avatarUrl: /avatars/5ba35c984d54eef4eacf11ebebafa3a0.svg
paper_authors.fullname: Junfeng Ni
paper_authors.type: user
paper_authors.user: JunfengNi
paper_authors._id: 67c185f46a31b8fe77434555
paper_authors.name: Song-Chun Zhu
paper_authors._id: 67c185f46a31b8fe77434556
paper_authors.name: Siyuan Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-28T12:35:02.710Z
paper_authors._id: 63c7a33121bd95f80ed74652
paper_authors.avatarUrl: /avatars/7dd59afea785a2bff0ec2b757abd474e.svg
paper_authors.fullname: Siyuan Huang
paper_authors.type: user
paper_authors.user: thuhsy
paper_title: Building Interactable Replicas of Complex Articulated Objects via
    Gaussian Splatting
paper_summary: Building articulated objects is a key challenge in computer vision. Existing
  methods often fail to effectively integrate information across different object
  states, limiting the accuracy of part-mesh reconstruction and part dynamics
  modeling, particularly for complex multi-part articulated objects. We introduce
  ArtGS, a novel approach that leverages 3D Gaussians as a flexible and efficient
  representation to address these issues. Our method incorporates canonical
  Gaussians with coarse-to-fine initialization and updates for aligning
  articulated part information across different object states, and employs a
  skinning-inspired part dynamics modeling module to improve both part-mesh
  reconstruction and articulation learning. Extensive experiments on both
  synthetic and real-world datasets, including a new benchmark for complex
  multi-part objects, demonstrate that ArtGS achieves state-of-the-art
  performance in joint parameter estimation and part mesh reconstruction. Our
  approach significantly improves reconstruction quality and efficiency,
  especially for multi-part articulated objects. Additionally, we provide
  comprehensive analyses of our design choices, validating the effectiveness of
  each component to highlight potential areas for future improvement.
paper_ai_keywords: 3D Gaussians canonical Gaussians coarse-to-fine initialization skinning-inspired part dynamics modeling output-mesh reconstruction articulation learning joint parameter estimation part mesh combining 3D Gaussians canonical Gaussians coarse-to-fine initialization skinning-inspired part dynamics modeling output-mesh reconstruction articulation learning joint parameter estimation part mesh combining