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Computer Science > Computer Vision and Pattern Recognition
Title: PIE-NeRF: Physics-based Interactive Elastodynamics with NeRF
(Submitted on 22 Nov 2023 (v1), last revised 27 Mar 2024 (this version, v2))
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 this https URL
Submission history
From: Yutao Feng [view email][v1] Wed, 22 Nov 2023 01:58:26 GMT (29862kb,D)
[v2] Wed, 27 Mar 2024 23:49:07 GMT (16200kb,D)
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