PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics

Tianyi Xie, Zeshun Zong, Yuxing Qiu, Xuan Li, Yutao Feng, Yin Yang, Chenfanfu Jiang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 4389-4398

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.