Computer Science > Computer Vision and Pattern Recognition
arXiv:2403.12438 (cs)
[Submitted on 19 Mar 2024]
Title:Precise-Physics Driven Text-to-3D Generation
View a PDF of the paper titled Precise-Physics Driven Text-to-3D Generation, by Qingshan Xu and 4 other authors
View PDFHTML (experimental)Abstract: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.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2403.12438 [cs.CV] |
| (orarXiv:2403.12438v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2403.12438 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Precise-Physics Driven Text-to-3D Generation, by Qingshan Xu and 4 other authors
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