We consider the problem of novel view synthesis (NVS) for dynamic scenes.
Recent neural approaches have accomplished exceptional NVS results for static
3D scenes, but extensions to 4D time-varying scenes remain non-trivial. Prior
efforts often encode dynamics by learning a canonical space plus implicit or
explicit deformation fields, which struggle in challenging scenarios like
sudden movements or capturing high-fidelity renderings. In this paper, we
introduce 4D Gaussian Splatting (4DGS), a novel method that represents dynamic
scenes with anisotropic 4D XYZT Gaussians, inspired by the success of 3D
Gaussian Splatting in static scenes. We model dynamics at each timestamp by
temporally slicing the 4D Gaussians, which naturally compose dynamic 3D
Gaussians and can be seamlessly projected into images. As an explicit
spatial-temporal representation, 4DGS demonstrates powerful capabilities for
modeling complicated dynamics and fine details, especially for scenes with
abrupt motions. We further implement our temporal slicing and splatting
techniques in a highly optimized CUDA acceleration framework, achieving
real-time inference rendering speeds of up to 277 FPS on an RTX 3090 GPU and
583 FPS on an RTX 4090 GPU. Rigorous evaluations on scenes with diverse motions
showcase the superior efficiency and effectiveness of 4DGS, which consistently
outperforms existing methods both quantitatively and qualitatively.

4D 高斯喷洒是一种在动态场景下表示动态场景的新方法，通过时间切片和投影技术实现了对复杂运动和细节的建模，并在实时渲染方面取得了显著的效果和效率优势。