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/.

翻译：我们证明，物理仿真可以与NeRF无缝集成，以生成真实世界物体的高质量弹性动力学。与现有方法不同，我们以无网格方式离散非线性超弹性，消除了对四面体网格或体素网格等中间辅助形状代理的需求。采用二次广义移动最小二乘法（Q-GMLS）来捕捉隐式模型上的非线性动力学和大变形。这种无网格集成使得复杂和共形形状的通用仿真成为可能。我们根据NeRF密度场自适应地放置最小二乘核，显著降低了非线性仿真的复杂度。因此，利用我们的方法可以方便地合成适用于多种超弹性材料的物理逼真动画，并实现交互式速率。更多信息，请访问我们的项目页面：https://fytalon.github.io/pienerf/。