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 generating high-fidelity renderings. In this paper, we introduce 4D Gaussian Splatting (4DRotorGS), 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, 4DRotorGS 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 4DRotorGS, which consistently outperforms existing methods both quantitatively and qualitatively.

翻译：本文研究动态场景的新视角合成问题。近期基于神经网络的静态三维场景新视角合成方法取得了卓越成果，但将其扩展至四维时变场景仍具挑战性。现有方法通常通过学习规范空间结合隐式或显式形变场来编码动态信息，但在处理剧烈运动或生成高保真渲染等复杂场景时存在局限。本文提出4D高斯溅射方法，受静态场景中3D高斯溅射成功的启发，采用各向异性四维XYZT高斯分布表征动态场景。我们通过对四维高斯分布进行时间切片来建模每一时刻的动态特性，该方法可自然组合成动态三维高斯分布并能够无缝投影至图像空间。作为一种显式的时空表征方法，4D-旋转高斯溅射展现出对复杂动态和精细细节的强大建模能力——特别适用于存在突变运动的场景。我们进一步在高度优化的CUDA加速框架中实现了时间切片与溅射技术，在RTX 3090 GPU上达到277 FPS、在RTX 4090 GPU上达到583 FPS的实时推理渲染速度。在多种运动场景上的严格评估表明，4D-旋转高斯溅射在定量与定性评估中均持续超越现有方法，展现出卓越的效率和性能优势。