PIE-NeRF: Physics-based Interactive Elastodynamics with NeRF

• URL: http://arxiv.org/abs/2311.13099v2
• Date: Wed, 27 Mar 2024 23:49:07 GMT
• Title: PIE-NeRF: Physics-based Interactive Elastodynamics with NeRF
• Authors: Yutao Feng, Yintong Shang, Xuan Li, Tianjia Shao, Chenfanfu Jiang, Yin Yang,
• Abstract summary: We show that physics-based simulations can be seamlessly integrated with NeRF to generate high-quality elastodynamics of real-world objects. A quadratic generalized moving least square (Q-GMLS) is employed to capture nonlinear dynamics and large deformation on the implicit model. We adaptively place the least-square kernels according to the NeRF density field to significantly reduce the complexity of the nonlinear simulation.
• Score: 29.6350855891474
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/.

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