Infinite Mobility: Scalable High-Fidelity Synthesis of Articulated
  Objects via Procedural Generation
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
67da22b75fe852c86d3c419b
Xinyu Lian
admin_assigned
2025-03-19T13:29:18.619Z
663b78429b8151660fbb66d4
/avatars/6664af07c38a1ce34fc6b2f3d41ca719.svg
Lian Xinyu
user
Lianxy
67da22b75fe852c86d3c419c
Zichao Yu
67da22b75fe852c86d3c419d
Ruiming Liang
admin_assigned
2025-03-19T13:29:34.205Z
66f4dde070850ff358a098e8
/avatars/d52973933e33379977c32c775477fc97.svg
Ruiming Liang
user
lrmbbj
67da22b75fe852c86d3c419e
Yitong Wang
67da22b75fe852c86d3c419f
Li Ray Luo
67da22b75fe852c86d3c41a0
Kaixu Chen
admin_assigned
2025-03-19T13:30:14.753Z
66b1dce83158b1fadffb0d9d
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/RbWKIxCjawVaSidwZLZA1.png
Kaixu Chen
user
kaixuChen
67da22b75fe852c86d3c41a1
Yuanzhen Zhou
admin_assigned
2025-03-19T13:30:45.060Z
642e4debb42737f9e1e2a6cb
https://cdn-avatars.huggingface.co/v1/production/uploads/642e4debb42737f9e1e2a6cb/yw_sfzSK7dxYe0Xym4Uea.png
Yuanzhen
user
zhouyuanzhen
67da22b75fe852c86d3c41a2
Qihong Tang
admin_assigned
2025-03-19T13:30:51.629Z
673dbb45df154a60e3aed0f1
/avatars/4a6f8996193d115ba3e5eae43be4c80e.svg
Qihong Tang
user
tangqh
67da22b75fe852c86d3c41a3
Xudong Xu
67da22b75fe852c86d3c41a4
Zhaoyang Lyu
admin_assigned
2025-03-19T13:29:05.257Z
63f2ec797ddf724fbcc75aee
/avatars/e93432ad11da703d46fe5e594d69f8c0.svg
Zhaoyang Lyu
user
ZhaoyangLyu
67da22b75fe852c86d3c41a5
Bo Dai
67da22b75fe852c86d3c41a6
Jiangmiao Pang
admin_assigned
2025-03-19T13:31:33.584Z
65783ee6ee33d547aecc3ffc
https://cdn-avatars.huggingface.co/v1/production/uploads/65783ee6ee33d547aecc3ffc/lWZX88c-0dCsN-yB9Jhlf.jpeg
Jiangmiao Pang
user
Jiangmiao
Infinite Mobility: Scalable High-Fidelity Synthesis of Articulated
  Objects via Procedural Generation
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
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.
articulated objects procedural generation state-of-the-art methods mesh quality physics property training data generative models