Novel-view synthesis (NVS) for dynamic scenes from 2D images presents significant challenges due to the spatial complexity and temporal variability of such scenes. Recently, inspired by the remarkable success of NVS using 3D Gaussian Splatting (3DGS), researchers have sought to extend 3D Gaussian models to four dimensions (4D) for dynamic novel-view synthesis. However, methods based on 4D rotation and scaling introduce spatiotemporal deformation into the 4D covariance matrix, necessitating the slicing of 4D Gaussians into 3D Gaussians. This process increases redundant computations as timestamps change-an inherent characteristic of dynamic scene rendering. Additionally, performing calculations on a four-dimensional matrix is computationally intensive. In this paper, we introduce Disentangled 4D Gaussian Splatting (Disentangled4DGS), a novel representation and rendering approach that disentangles temporal and spatial deformations, thereby eliminating the reliance on 4D matrix computations. We extend the 3DGS rendering process to 4D, enabling the projection of temporal and spatial deformations into dynamic 2D Gaussians in ray space. Consequently, our method facilitates faster dynamic scene synthesis. Moreover, it reduces storage requirements by at least 4.5\% due to our efficient presentation method. Our approach achieves an unprecedented average rendering speed of 343 FPS at a resolution of $1352\times1014$ on an RTX 3090 GPU, with experiments across multiple benchmarks demonstrating its competitive performance in both monocular and multi-view scenarios.

翻译：基于二维图像的动态场景新视角合成（NVS）因其空间复杂性和时间变异性而面临重大挑战。近期，受三维高斯泼溅（3DGS）在新视角合成领域显著成功的启发，研究者们尝试将三维高斯模型扩展至四维（4D）以实现动态新视角合成。然而，基于四维旋转与缩放的方法会在四维协方差矩阵中引入时空形变，这要求将四维高斯切片为三维高斯。该过程会随着时间戳变化（动态场景渲染的固有特性）增加冗余计算。此外，对四维矩阵进行计算在计算上十分密集。本文提出解耦四维高斯泼溅（Disentangled4DGS），这是一种新颖的表征与渲染方法，它将时间与空间形变解耦，从而消除了对四维矩阵计算的依赖。我们将3DGS的渲染过程扩展至四维，使得时间和空间形变能够投影到射线空间中的动态二维高斯上。因此，我们的方法能够实现更快的动态场景合成。此外，得益于我们高效的表征方法，存储需求至少降低了4.5%。我们的方法在RTX 3090 GPU上以$1352\times1014$分辨率实现了前所未有的平均渲染速度343 FPS，在多个基准测试上的实验表明，其在单目和多视图场景中均具有竞争力的性能。