Precise-Physics Driven Text-to-3D Generation
This addresses the issue of generating physically realistic 3D models for applications like virtual modeling, though it appears incremental by adding physics constraints to existing methods.
The paper tackles the problem of text-to-3D generation lacking precise physics perception, which hinders real-world practicality, and proposes Phy3DGen, a method that integrates solid mechanics analysis to optimize 3D shapes, resulting in improved geometric plausibility and physics conformity.
Text-to-3D generation has shown great promise in generating novel 3D content based on given text prompts. However, existing generative methods mostly focus on geometric or visual plausibility while ignoring precise physics perception for the generated 3D shapes. This greatly hinders the practicality of generated 3D shapes in real-world applications. In this work, we propose Phy3DGen, a precise-physics-driven text-to-3D 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 conform to the laws of physics. 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 precise physics information about 3D shapes at the same time. Experimental results demonstrate that our method can consider both geometric plausibility and precise physics perception, further bridging 3D virtual modeling and precise physical worlds.