用程序生成法批量制作高保真可动物体，省时高效且质量媲美人工标注。
Infinite Mobility: Scalable High-Fidelity Synthesis of Articulated Objects via Procedural Generation
- 通过程序化生成技术自动构造复杂可动物体，无需依赖大量训练数据或繁琐仿真。
- 用户测试与定量评估显示，生成物体在物理特性和网格质量上优于现有方法。
- 生成数据可直接用于训练生成模型，适合需要大规模高质量数据的研究者。
具身智能相关任务亟需大规模高质量的可动物体。现有方法多为数据驱动或基于仿真的，受限于训练数据规模与质量，或仿真精度与人力成本。本文提出 Infinite Mobility，一种通过程序化生成合成高保真可动物体的新方法。用户研究与定量评估表明，该方法生成结果在物理属性和网格质量上均超越当前最先进水平，且接近人工标注数据集表现。此外，其合成数据可作为生成模型的训练数据，支持后续规模化应用。代码已公开于 https://github.com/Intern-Nexus/Infinite-Mobility。
原文摘要 · Abstract (English)
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
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