While dynamic novel view synthesis from 2D videos has seen progress, achieving efficient reconstruction and rendering of dynamic scenes remains a challenging task. In this paper, we introduce Disentangled 4D Gaussian Splatting (Disentangled4DGS), a novel representation and rendering pipeline that achieves real-time performance without compromising visual fidelity. Disentangled4DGS decouples the temporal and spatial components of 4D Gaussians, avoiding the need for slicing first and four-dimensional matrix calculations in prior methods. By projecting temporal and spatial deformations into dynamic 2D Gaussians and deferring temporal processing, we minimize redundant computations of 4DGS. Our approach also features a gradient-guided flow loss and temporal splitting strategy to reduce artifacts. Experiments demonstrate a significant improvement in rendering speed and quality, achieving 343 FPS when render 1352*1014 resolution images on a single RTX3090 while reducing storage requirements by at least 4.5%. Our approach sets a new benchmark for dynamic novel view synthesis, outperforming existing methods on both multi-view and monocular dynamic scene datasets.

翻译：尽管基于二维视频的动态新视角合成已取得进展，但实现动态场景的高效重建与渲染仍是一项具有挑战性的任务。本文提出解耦四维高斯泼溅（Disentangled4DGS），一种新颖的表征与渲染流程，在保持视觉保真度的同时实现了实时性能。Disentangled4DGS将四维高斯的时间与空间分量解耦，避免了现有方法中需先进行切片及四维矩阵计算的需求。通过将时空变形投影至动态二维高斯并延迟时间维处理，我们最小化了四维高斯泼溅的冗余计算。该方法还采用梯度引导的光流损失函数与时间分割策略以减少伪影。实验表明，在单张RTX3090显卡上渲染1352*1014分辨率图像时，渲染速度与质量均有显著提升，达到343 FPS，同时存储需求降低至少4.5%。本方法为动态新视角合成设立了新基准，在多视角与单目动态场景数据集上均优于现有方法。