PIE-NeRF: Physics-based Interactive Elastodynamics with NeRF
This enables interactive, high-quality elastodynamic simulations for complex shapes in computer graphics and related fields, representing a novel integration rather than an incremental improvement.
The paper tackles the problem of integrating physics-based simulations with NeRF for elastodynamics of real-world objects, achieving physically realistic animations at an interactive rate without needing intermediate shape proxies like tetrahedral meshes.
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/.