Dynamic 4D Gaussian Splatting (4DGS) effectively extends the high-speed rendering capabilities of 3D Gaussian Splatting (3DGS) to represent volumetric videos. However, the large number of Gaussians, substantial temporal redundancies, and especially the absence of an entropy-aware compression framework result in large storage requirements. Consequently, this poses significant challenges for practical deployment, efficient edge-device processing, and data transmission. In this paper, we introduce a novel end-to-end RD-optimized compression framework tailored for 4DGS, aiming to enable flexible, high-fidelity rendering across varied computational platforms. Leveraging Fully Explicit Dynamic Gaussian Splatting (Ex4DGS), one of the state-of-the-art 4DGS methods, as our baseline, we start from the existing 3DGS compression methods for compatibility while effectively addressing additional challenges introduced by the temporal axis. In particular, instead of storing motion trajectories independently per point, we employ a wavelet transform to reflect the real-world smoothness prior, significantly enhancing storage efficiency. This approach yields significantly improved compression ratios and provides a user-controlled balance between compression efficiency and rendering quality. Extensive experiments demonstrate the effectiveness of our method, achieving up to 91$\times$ compression compared to the original Ex4DGS model while maintaining high visual fidelity. These results highlight the applicability of our framework for real-time dynamic scene rendering in diverse scenarios, from resource-constrained edge devices to high-performance environments. The source code is available at https://github.com/HyeongminLEE/RD4DGS.

翻译：动态4D高斯泼溅（4DGS）有效地将3D高斯泼溅（3DGS）的高速渲染能力扩展至体视频表示。然而，大量高斯函数、显著的时间冗余，尤其是缺乏熵感知压缩框架，导致其存储需求巨大。因此，这对实际部署、边缘设备高效处理及数据传输构成了重大挑战。本文提出一种专为4DGS设计的新型端到端率失真优化压缩框架，旨在实现跨不同计算平台的灵活高保真渲染。我们以当前最先进的4DGS方法之一——全显式动态高斯泼溅（Ex4DGS）作为基线，从现有3DGS压缩方法出发以确保兼容性，同时有效应对时间轴引入的额外挑战。具体而言，我们采用小波变换来反映现实世界平滑性先验，而非独立存储每个点的运动轨迹，从而显著提升存储效率。该方法大幅提高了压缩比，并在压缩效率与渲染质量之间提供了用户可控的平衡。大量实验证明了本方法的有效性，相比原始Ex4DGS模型实现了高达91倍的压缩，同时保持高视觉保真度。这些结果凸显了本框架在从资源受限边缘设备到高性能环境等多种场景中实时动态场景渲染的适用性。源代码发布于https://github.com/HyeongminLEE/RD4DGS。