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PIE-NeRF: Physics-based Interactive Elastodynamics with NeRF

Yutao Feng · Yintong Shang · Xuan Li · Tianjia Shao · Chenfanfu Jiang · Yin Yang

Arch 4A-E Poster #418
[ ] [ Project Page ]
Wed 19 Jun 10:30 a.m. PDT — noon PDT


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.

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