PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866909168761307136 |
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| author | Xie, Tianyi Zong, Zeshun Qiu, Yuxing Li, Xuan Feng, Yutao Yang, Yin Jiang, Chenfanfu |
| author_facet | Xie, Tianyi Zong, Zeshun Qiu, Yuxing Li, Xuan Feng, Yutao Yang, Yin Jiang, Chenfanfu |
| contents | 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/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_12198 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics Xie, Tianyi Zong, Zeshun Qiu, Yuxing Li, Xuan Feng, Yutao Yang, Yin Jiang, Chenfanfu Graphics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning 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/ |
| title | PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics |
| topic | Graphics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2311.12198 |