Infinite Mobility: Scalable High-Fidelity Synthesis of Articulated Objects via Procedural Generation
This addresses the need for scalable, high-quality articulated objects in embodied AI tasks, offering a novel approach that is not incremental.
The paper tackles the problem of generating large-scale, high-fidelity articulated objects for embodied AI by proposing a procedural generation method, achieving results that excel state-of-the-art methods and are comparable to human-annotated datasets in physics and mesh quality.
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