Physically Plausible and Temporally Coherent Motion in 4D Gaussian Splatting

Synthesizing novel views of dynamic scenes is challenging in computer vision. Without explicit constraints between Gaussian distributions and motion, 3DGS struggles to produce physically realistic renderings, a limitation amplified in 4DGS. The temporal dimension increases complexity and leads to geometric degradation. Although higher point cloud density can improve accuracy, it notably increases computational cost. To achieve physically consistent and temporally coherent rendering of dynamic scenes, we introduce PhysMotion4DGS, a physics-aware 4D Gaussian Splatting framework. It introduces 4D physical realism consistency regularization, which constrains the motion velocity of Gaussian distributions using cues from both image space and 3D physical space. Furthermore, PhysMotion4DGS introduces temporal consistency regularization to ensure smooth, continuous motion over time, preventing unnatural dynamics. To capture both global structure and fine details, we adopt a progressive multi-scale training strategy that optimizes Gaussian distributions in stages, achieving high-quality rendering without increasing point cloud size. Experimental results show that PhysMotion4DGS surpasses previous methods in the stability and realism of motion representation in dynamic scenes with comparable rendering speeds and training time.