Looks great, functions better: Physics compliance text-to-3D shape generation
Abstract
Text-to-3D shape generation has shown great promise in generating novel 3D content based on given text prompts. However, existing generative methods mainly consider geometric or visual plausibility while ignoring functionality for the generated 3D shapes. This greatly hinders the practicality of generated 3D shapes in real-world applications. Towards physical AI, we propose Fun3D, a physics-compliant functional text-to-3D shape generation method. By analyzing the solid mechanics of generated 3D shapes, we reveal that the 3D shapes generated by existing text-to-3D generation methods are impractical for real-world applications, as the generated 3D shapes do not comply with the physical laws. To this end, we leverage 3D diffusion models to provide 3D shape priors and design a data-driven differentiable physics layer to optimize 3D shape priors with solid mechanics. This allows us to optimize geometry efficiently and learn physical information about 3D shapes at the same time. Experimental results demonstrate that our method can consider both geometric plausibility and functional requirement, further bridging 3D virtual modeling and physical worlds to advance physical AI.
Authors: Qingshan Xu, Jiao Liu, Melvin Wong, Caishun Chen, Yew-Soon Ong