PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics

Tianyi Xie, Zeshun Zong, Yuxing Qiu, Xuan Li, Yutao Feng +2 more
11/1/2023

No official code found. Best implementation: XPandora/PhysGaussian (1,423 stars, last commit 2026-01-21).

Abstract

We introduce PhysGaussian, a new method that seamlessly integrates physically grounded Newtonian dynamics within 3D Gaussians to achieve high-quality novel motion synthe-sis. Employing a custom 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 (WS2).” 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. Our project page is at: https://xpandora.github.io/PhysGaussian/

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[CVPR 2024 Highlight] PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics

1,42371Shell, PythonNov 13, 20239 months ago

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Cite this paper

@article{xie2023physgaussian,
  title  = {PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics},
  author = {Tianyi Xie and Zeshun Zong and Yuxing Qiu and Xuan Li and Yutao Feng and Yin Yang and Chenfanfu Jiang},
  year   = {2023},
  eprint = {2311.12198},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/pdf/2311.12198},
  journal = {CVPR 2023 2023}
}

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