提出一种无需优化的动态3D高斯点云压缩框架，实现17倍以上压缩率。
D-FCGS: Feedforward Compression of Dynamic Gaussian Splatting for Free-Viewpoint Videos
- 采用帧组结构与I-P编码，用稀疏控制点提取跨帧运动张量。
- 双先验熵模型融合超先验与时空先验，提升码率估计精度。
- 适合追求高效传输的沉浸式视频应用，零样本泛化能力强。
自由视角视频（FVV）可提供沉浸式3D体验，但动态3D表示的高效压缩仍是重大挑战。现有动态3D高斯点云方法将重建与依赖优化的压缩及定制化运动格式耦合，限制了泛化性与标准化。为此，本文提出D-FCGS，一种面向动态高斯点云的前馈压缩框架。核心创新包括：(1) 标准化的帧组（GoF）结构与I-P编码，利用稀疏控制点提取跨帧运动张量；(2) 融合超先验与时空先验的双先验感知熵模型，实现精准码率估计；(3) 控制点引导的运动补偿机制与精炼网络，提升视图一致性保真度。在多视角视频生成的高斯帧上训练，D-FCGS实现零样本跨场景泛化。实验表明，其率失真性能媲美基于优化的方法，在保持各视角视觉质量的前提下，压缩比超过基线17倍。该工作推动了动态3DGS的前馈压缩发展，为沉浸式应用的可扩展传输与存储奠定基础。
原文摘要 · Abstract (English)
Free-Viewpoint Video (FVV) enables immersive 3D experiences, but efficient compression of dynamic 3D representation remains a major challenge. Existing dynamic 3D Gaussian Splatting methods couple reconstruction with optimization-dependent compression and customized motion formats, limiting generalization and standardization. To address this, we propose D-FCGS, a novel Feedforward Compression framework for Dynamic Gaussian Splatting. Key innovations include: (1) a standardized Group-of-Frames (GoF) structure with I-P coding, leveraging sparse control points to extract inter-frame motion tensors; (2) a dual prior-aware entropy model that fuses hyperprior and spatial-temporal priors for accurate rate estimation; (3) a control-point-guided motion compensation mechanism and refinement network to enhance view-consistent fidelity. Trained on Gaussian frames derived from multi-view videos, D-FCGS generalizes across diverse scenes in a zero-shot fashion. Experiments show that it matches the rate-distortion performance of optimization-based methods, achieving over 17 times compression compared to the baseline while preserving visual quality across viewpoints. This work advances feedforward compression of dynamic 3DGS, facilitating scalable FVV transmission and storage for immersive applications.
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