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PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics

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arxiv 2311.12198 v3 pith:67HA6SYQ submitted 2023-11-20 cs.GR cs.AIcs.CVcs.LG

Tianyi Xie , Zeshun Zong , Yuxing Qiu , Xuan Li , Yutao Feng , Yin Yang , Chenfanfu Jiang This is my paper · ORCID
classification cs.GRcs.AIcs.CVcs.LG
keywords methodphysgaussiandynamicsgaussiangaussianskernelsnovelphysically
verification ladder T0 review T1 audit T2 compute T3 formal
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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 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 (WS$^2$)." Our method demonstrates exceptional versatility across a wide variety of materials--including elastic entities, 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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Forward citations

Cited by 5 Pith papers

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  3. Pixie: Fast and Generalizable Supervised Learning of 3D Physics from Pixels

    cs.CV 2025-08 reject novelty 5.0 of 10

    A supervised 3D U-Net predicts per-voxel material fields from CLIP feature grids, enabling fast MPM-based animation, but the reported evidence depends on pseudo-labels and a VLM judge from the same model family as the...

  4. CTRL-GS: Cascaded Temporal Residue Learning for 4D Gaussian Splatting

    cs.CV 2025-05 conditional novelty 5.0 of 10

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  5. Enhancing non-Rigid 3D Model Deformations Using Mesh-based Gaussian Splatting

    cs.GR 2025-07 reject novelty 2.0 of 10

    A proposal to combine 3D Gaussian splatting, SAM segmentation, GS2Mesh conversion, LLM-based material assignment, and XPBD physics into a mesh-based editing pipeline, with no experimental validation.

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