Infinite Mobility: Scalable High-Fidelity Synthesis of Articulated
  Objects via Procedural Generation
Authors: name: Xinyu Lian, name: Zichao Yu, name: Ruiming Liang, name: Yitong Wang, name: Li Ray Luo, name: Kaixu Chen, name: Yuanzhen Zhou, name: Qihong Tang, name: Xudong Xu, name: Zhaoyang Lyu, name: Bo Dai, name: Jiangmiao Pang

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