3D Gaussian Splatting (3DGS) has emerged as an efficient and high-fidelity paradigm for novel view synthesis. To adapt 3DGS for dynamic content, deformable 3DGS incorporates temporally deformable primitives with learnable latent embeddings to capture complex motions. Despite its impressive performance, the high-dimensional embeddings and vast number of primitives lead to substantial storage requirements. In this paper, we introduce a \textbf{Light}weight \textbf{4}D\textbf{GS} framework, called Light4GS, that employs significance pruning with a deep context model to provide a lightweight storage-efficient dynamic 3DGS representation. The proposed Light4GS is based on 4DGS that is a typical representation of deformable 3DGS. Specifically, our framework is built upon two core components: (1) a spatio-temporal significance pruning strategy that eliminates over 64\% of the deformable primitives, followed by an entropy-constrained spherical harmonics compression applied to the remainder; and (2) a deep context model that integrates intra- and inter-prediction with hyperprior into a coarse-to-fine context structure to enable efficient multiscale latent embedding compression. Our approach achieves over 120x compression and increases rendering FPS up to 20\% compared to the baseline 4DGS, and also superior to frame-wise state-of-the-art 3DGS compression methods, revealing the effectiveness of our Light4GS in terms of both intra- and inter-prediction methods without sacrificing rendering quality.

翻译：三维高斯溅射（3DGS）已成为新颖视角合成领域一种高效且高保真的范式。为使3DGS适应动态内容，可变形3DGS通过引入具有可学习潜在嵌入的时序可变形基元来捕捉复杂运动。尽管其性能卓越，但高维嵌入与海量基元导致存储需求巨大。本文提出一种轻量化的4DGS框架——Light4GS，该框架采用基于深度上下文模型的重要性剪枝技术，构建了存储高效的轻量化动态3DGS表征。所提Light4GS基于可变形3DGS的典型表征——4DGS实现。具体而言，本框架由两个核心组件构成：（1）时空重要性剪枝策略，可消除超过64%的可变形基元，并对剩余部分实施熵约束球谐函数压缩；（2）深度上下文模型，将帧内-帧间预测与超先验整合至由粗到精的上下文结构中，实现高效的多尺度潜在嵌入压缩。相较于基线4DGS，本方法实现了超过120倍的压缩比，渲染帧率提升达20%，且性能优于当前逐帧最优的3DGS压缩方法，这证明了Light4GS在保持渲染质量的前提下，其帧内与帧间预测方法的有效性。