Precise-Physics Driven Text-to-3D Generation
Qingshan Xu Jiao Liu Melvin Wong Caishun Chen Yew-Soon Ong
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.