  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/.
