ParticleGS: Particle-Based Dynamics Modeling of 3D Gaussians for Prior-free Motion Extrapolation

Abstract: This paper aims to model the dynamics of 3D Gaussians from visual observations to support temporal extrapolation. Existing dynamic 3D reconstruction methods often struggle to effectively learn underlying dynamics or rely heavily on manually defined physical priors, which limits their extrapolation capabilities. To address this issue, we propose a novel dynamic 3D Gaussian Splatting prior-free motion extrapolation framework based on particle dynamics systems. The core advantage of our method lies in its ability to learn differential equations that describe the dynamics of 3D Gaussians, and follow them during future frame extrapolation. Instead of simply fitting to the observed visual frame sequence, we aim to more effectively model the gaussian particle dynamics system. To this end, we introduce a dynamics latent state vector into the standard Gaussian kernel and design a dynamics latent space encoder to extract initial state. Subsequently, we introduce a Neural ODEs-based dynamics module that models the temporal evolution of Gaussian in dynamics latent space. Finally, a Gaussian kernel space decoder is used to decode latent state at the specific time step into the deformation. Experimental results demonstrate that the proposed method achieves comparable rendering quality with existing approaches in reconstruction tasks, and significantly outperforms them in future frame extrapolation. Our code is available at https://github.com/QuanJinSheng/ParticleGS.