Infinite Mobility: Scalable High-Fidelity Synthesis of Articulated Objects via Procedural Generation

Lian Xinyu, zichao, Ruiming Liang, Yitong Wang, Li Luo, Kaixu Chen, Yuanzhen, Qihong Tang, Xudong Xu, Zhaoyang Lyu, Bo Dai, Jiangmiao Pang

Infinite Mobility: Scalable High-Fidelity Synthesis of Articulated Objects via Procedural Generation: 30 upvotes on Hugging Face Daily Papers, #6 of 31 papers on 2025-03-19. Day-by-day upvote history.

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 page on Hugging Face · arXiv

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