PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics
Abstract
We introduce PhysGaussian a new method that seamlessly integrates physically grounded Newtonian dynamics within 3D Gaussians to achieve high-quality novel motion synthesis. Employing a customized Material Point Method (MPM) our approach enriches 3D Gaussian kernels with physically meaningful kinematic deformation and mechanical stress attributes all evolved in line with continuum mechanics principles. A defining characteristic of our method is the seamless integration between physical simulation and visual rendering: both components utilize the same 3D Gaussian kernels as their discrete representations. This negates the necessity for triangle/tetrahedron meshing marching cubes cage meshes or any other geometry embedding highlighting the principle of "what you see is what you simulate (WS^2)". Our method demonstrates exceptional versatility across a wide variety of materials--including elastic entities plastic metals non-Newtonian fluids and granular materials--showcasing its strong capabilities in creating diverse visual content with novel viewpoints and movements.
Cite
Text
Xie et al. "PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics." Conference on Computer Vision and Pattern Recognition, 2024. doi:10.1109/CVPR52733.2024.00420Markdown
[Xie et al. "PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics." Conference on Computer Vision and Pattern Recognition, 2024.](https://mlanthology.org/cvpr/2024/xie2024cvpr-physgaussian/) doi:10.1109/CVPR52733.2024.00420BibTeX
@inproceedings{xie2024cvpr-physgaussian,
title = {{PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics}},
author = {Xie, Tianyi and Zong, Zeshun and Qiu, Yuxing and Li, Xuan and Feng, Yutao and Yang, Yin and Jiang, Chenfanfu},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2024},
pages = {4389-4398},
doi = {10.1109/CVPR52733.2024.00420},
url = {https://mlanthology.org/cvpr/2024/xie2024cvpr-physgaussian/}
}