Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusion
<|reference_start|>Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusion: In recent years, there has been rapid development in 3D generation models, opening up new possibilities for applications such as simulating the dynamic movements of 3D objects and customizing their behaviors. However, current 3D generative models tend to focus only on surface features such as color and shape, neglecting the inherent physical properties that govern the behavior of objects in the real world. To accurately simulate physics-aligned dynamics, it is essential to predict the physical properties of materials and incorporate them into the behavior prediction process. Nonetheless, predicting the diverse materials of real-world objects is still challenging due to the complex nature of their physical attributes. In this paper, we propose \textbf{Physics3D}, a novel method for learning various physical properties of 3D objects through a video diffusion model. Our approach involves designing a highly generalizable physical simulation system based on a viscoelastic material model, which enables us to simulate a wide range of materials with high-fidelity capabilities. Moreover, we distill the physical priors from a video diffusion model that contains more understanding of realistic object materials. Extensive experiments demonstrate the effectiveness of our method with both elastic and plastic materials. Physics3D shows great potential for bridging the gap between the physical world and virtual neural space, providing a better integration and application of realistic physical principles in virtual environments. Project page: https://liuff19.github.io/Physics3D.<|reference_end|>
@article{liu2024physics3d:,
    title={Physics3D: Learning Physical Properties of 3D Gaussians via Video
  Diffusion},
    author={Fangfu Liu, Hanyang Wang, Shunyu Yao, Shengjun Zhang, Jie Zhou and
  Yueqi Duan},
    journal={arXiv preprint arXiv:2406.04338},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04338},
    primaryClass={cs.CV cs.AI cs.GR}
}