title: Infinite Mobility: Scalable High-Fidelity Synthesis of Articulated
    Objects via Procedural Generation
summary: Large-scale articulated objects with high quality are desperately needed for
  multiple tasks related to embodied AI. Most existing methods for creating
  articulated objects are either data-driven or simulation based, which are
  limited by the scale and quality of the training data or the fidelity and heavy
  labour of the simulation. In this paper, we propose Infinite Mobility, a novel
  method for synthesizing high-fidelity articulated objects through procedural
  generation. User study and quantitative evaluation demonstrate that our method
  can produce results that excel current state-of-the-art methods and are
  comparable to human-annotated datasets in both physics property and mesh
  quality. Furthermore, we show that our synthetic data can be used as training
  data for generative models, enabling next-step scaling up. Code is available at
  https://github.com/Intern-Nexus/Infinite-Mobility
paper_authors._id: 67da22b75fe852c86d3c419b
paper_authors.name: Xinyu Lian
paper_authors.status: admin_assigned
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paper_authors.fullname: Lian Xinyu
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paper_authors.user: Lianxy
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paper_authors.name: Zichao Yu
paper_authors._id: 67da22b75fe852c86d3c419d
paper_authors.name: Ruiming Liang
paper_authors.status: admin_assigned
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paper_authors.fullname: Ruiming Liang
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paper_authors.user: lrmbbj
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paper_authors.name: Yitong Wang
paper_authors._id: 67da22b75fe852c86d3c419f
paper_authors.name: Li Ray Luo
paper_authors._id: 67da22b75fe852c86d3c41a0
paper_authors.name: Kaixu Chen
paper_authors.status: admin_assigned
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paper_authors.fullname: Kaixu Chen
paper_authors.type: user
paper_authors.user: kaixuChen
paper_authors._id: 67da22b75fe852c86d3c41a1
paper_authors.name: Yuanzhen Zhou
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paper_authors.fullname: Yuanzhen
paper_authors.type: user
paper_authors.user: zhouyuanzhen
paper_authors._id: 67da22b75fe852c86d3c41a2
paper_authors.name: Qihong Tang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-19T13:30:51.629Z
paper_authors._id: 673dbb45df154a60e3aed0f1
paper_authors.avatarUrl: /avatars/4a6f8996193d115ba3e5eae43be4c80e.svg
paper_authors.fullname: Qihong Tang
paper_authors.type: user
paper_authors.user: tangqh
paper_authors._id: 67da22b75fe852c86d3c41a3
paper_authors.name: Xudong Xu
paper_authors._id: 67da22b75fe852c86d3c41a4
paper_authors.name: Zhaoyang Lyu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-19T13:29:05.257Z
paper_authors._id: 63f2ec797ddf724fbcc75aee
paper_authors.avatarUrl: /avatars/e93432ad11da703d46fe5e594d69f8c0.svg
paper_authors.fullname: Zhaoyang Lyu
paper_authors.type: user
paper_authors.user: ZhaoyangLyu
paper_authors._id: 67da22b75fe852c86d3c41a5
paper_authors.name: Bo Dai
paper_authors._id: 67da22b75fe852c86d3c41a6
paper_authors.name: Jiangmiao Pang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-19T13:31:33.584Z
paper_authors._id: 65783ee6ee33d547aecc3ffc
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paper_authors.fullname: Jiangmiao Pang
paper_authors.type: user
paper_authors.user: Jiangmiao
paper_title: Infinite Mobility: Scalable High-Fidelity Synthesis of Articulated
    Objects via Procedural Generation
paper_summary: Large-scale articulated objects with high quality are desperately needed for
  multiple tasks related to embodied AI. Most existing methods for creating
  articulated objects are either data-driven or simulation based, which are
  limited by the scale and quality of the training data or the fidelity and heavy
  labour of the simulation. In this paper, we propose Infinite Mobility, a novel
  method for synthesizing high-fidelity articulated objects through procedural
  generation. User study and quantitative evaluation demonstrate that our method
  can produce results that excel current state-of-the-art methods and are
  comparable to human-annotated datasets in both physics property and mesh
  quality. Furthermore, we show that our synthetic data can be used as training
  data for generative models, enabling next-step scaling up. Code is available at
  https://github.com/Intern-Nexus/Infinite-Mobility
paper_ai_summary: A novel method, Infinite Mobility, synthesizes high-fidelity articulated objects using procedural generation, excelling state-of-the-art methods in quality and usability for training generative models.
paper_ai_keywords: articulated objects procedural generation state-of-the-art methods mesh quality physics property training data generative models