DreamPhysics: Learning Physics-Based 3D Dynamics with Video Diffusion Priors

• URL: http://arxiv.org/abs/2406.01476v3
• Date: Wed, 18 Dec 2024 13:47:38 GMT
• Title: DreamPhysics: Learning Physics-Based 3D Dynamics with Video Diffusion Priors
• Authors: Tianyu Huang, Haoze Zhang, Yihan Zeng, Zhilu Zhang, Hui Li, Wangmeng Zuo, Rynson W. H. Lau,
• Abstract summary: We propose to learn the physical properties of a material field with video diffusion priors. We then utilize a physics-based Material-Point-Method simulator to generate 4D content with realistic motions.
• Score: 75.83647027123119
• License:
• Abstract: Dynamic 3D interaction has been attracting a lot of attention recently. However, creating such 4D content remains challenging. One solution is to animate 3D scenes with physics-based simulation, which requires manually assigning precise physical properties to the object or the simulated results would become unnatural. Another solution is to learn the deformation of 3D objects with the distillation of video generative models, which, however, tends to produce 3D videos with small and discontinuous motions due to the inappropriate extraction and application of physics priors. In this work, to combine the strengths and complementing shortcomings of the above two solutions, we propose to learn the physical properties of a material field with video diffusion priors, and then utilize a physics-based Material-Point-Method (MPM) simulator to generate 4D content with realistic motions. In particular, we propose motion distillation sampling to emphasize video motion information during distillation. In addition, to facilitate the optimization, we further propose a KAN-based material field with frame boosting. Experimental results demonstrate that our method enjoys more realistic motions than state-of-the-arts do.

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