PIE-NeRF: Physics-based Interactive Elastodynamics with NeRF
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911817523003392 |
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| author | Feng, Yutao Shang, Yintong Li, Xuan Shao, Tianjia Jiang, Chenfanfu Yang, Yin |
| author_facet | Feng, Yutao Shang, Yintong Li, Xuan Shao, Tianjia Jiang, Chenfanfu Yang, Yin |
| contents | We show that physics-based simulations can be seamlessly integrated with NeRF to generate high-quality elastodynamics of real-world objects. Unlike existing methods, we discretize nonlinear hyperelasticity in a meshless way, obviating the necessity for intermediate auxiliary shape proxies like a tetrahedral mesh or voxel grid. A quadratic generalized moving least square (Q-GMLS) is employed to capture nonlinear dynamics and large deformation on the implicit model. Such meshless integration enables versatile simulations of complex and codimensional shapes. We adaptively place the least-square kernels according to the NeRF density field to significantly reduce the complexity of the nonlinear simulation. As a result, physically realistic animations can be conveniently synthesized using our method for a wide range of hyperelastic materials at an interactive rate. For more information, please visit our project page at https://fytalon.github.io/pienerf/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_13099 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | PIE-NeRF: Physics-based Interactive Elastodynamics with NeRF Feng, Yutao Shang, Yintong Li, Xuan Shao, Tianjia Jiang, Chenfanfu Yang, Yin Computer Vision and Pattern Recognition Artificial Intelligence Graphics Machine Learning We show that physics-based simulations can be seamlessly integrated with NeRF to generate high-quality elastodynamics of real-world objects. Unlike existing methods, we discretize nonlinear hyperelasticity in a meshless way, obviating the necessity for intermediate auxiliary shape proxies like a tetrahedral mesh or voxel grid. A quadratic generalized moving least square (Q-GMLS) is employed to capture nonlinear dynamics and large deformation on the implicit model. Such meshless integration enables versatile simulations of complex and codimensional shapes. We adaptively place the least-square kernels according to the NeRF density field to significantly reduce the complexity of the nonlinear simulation. As a result, physically realistic animations can be conveniently synthesized using our method for a wide range of hyperelastic materials at an interactive rate. For more information, please visit our project page at https://fytalon.github.io/pienerf/. |
| title | PIE-NeRF: Physics-based Interactive Elastodynamics with NeRF |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Graphics Machine Learning |
| url | https://arxiv.org/abs/2311.13099 |