Neural scene representations, such as 3D Gaussian Splatting (3DGS), have enabled high-quality neural rendering; however, their large storage and transmission costs hinder deployment in resource-constrained environments. Existing compression methods either rely on costly optimization, which is slow and scene-specific, or adopt training-free pruning and quantization, which degrade rendering quality under high compression ratios. In contrast, recent data-driven approaches provide a promising direction to overcome this trade-off, enabling efficient compression while preserving high rendering quality. We introduce ExGS, a novel feed-forward framework that unifies Universal Gaussian Compression (UGC) with GaussPainter for Extreme 3DGS compression. UGC performs re-optimization-free pruning to aggressively reduce Gaussian primitives while retaining only essential information, whereas GaussPainter leverages powerful diffusion priors with mask-guided refinement to restore high-quality renderings from heavily pruned Gaussian scenes. Unlike conventional inpainting, GaussPainter not only fills in missing regions but also enhances visible pixels, yielding substantial improvements in degraded renderings. To ensure practicality, it adopts a lightweight VAE and a one-step diffusion design, enabling real-time restoration. Our framework can even achieve over 100X compression (reducing a typical 354.77 MB model to about 3.31 MB) while preserving fidelity and significantly improving image quality under challenging conditions. These results highlight the central role of diffusion priors in bridging the gap between extreme compression and high-quality neural rendering.
神经场景表示（Neural Scene Representation）方法，如三维高斯溅射（3D Gaussian Splatting, 3DGS），已实现高质量的神经渲染。然而，其庞大的存储与传输开销严重制约了在资源受限环境中的部署。现有压缩方法要么依赖于代价高昂的优化过程（速度慢且场景特定），要么采用无训练（training-free）的剪枝与量化策略，在高压缩比下会显著损害渲染质量。相比之下，近期基于数据驱动的压缩方法为打破这一权衡提供了新的方向，使得在保持高渲染质量的同时实现高效压缩成为可能。本文提出 ExGS，一种统一了通用高斯压缩（Universal Gaussian Compression, UGC）与高斯修复器（GaussPainter）的新型前馈框架，用于实现极致的 3DGS 压缩。UGC 通过无再优化的剪枝（re-optimization-free pruning）极大地减少高斯基元数量，仅保留关键信息；而 GaussPainter 则利用强大的扩散先验（diffusion priors）与掩膜引导的精修（mask-guided refinement），在严重剪枝的高斯场景中恢复高质量渲染结果。不同于传统的图像修补（inpainting），GaussPainter 不仅填补缺失区域，还能增强可见像素，从而显著改善退化渲染效果。为保证实用性，该方法采用轻量化的变分自编码器（VAE）与一步扩散（one-step diffusion）设计，实现实时恢复。实验表明，该框架可实现超过 100 倍压缩率（将典型的 354.77 MB 模型压缩至约 3.31 MB），同时在复杂条件下保持高保真度并显著提升图像质量。结果凸显了扩散先验在连接极限压缩与高质量神经渲染之间的核心作用。