GFix：感知增强的高斯泼溅视频压缩
GFix: Perceptually Enhanced Gaussian Splatting Video Compression
AI总结：
本文提出GFix框架，通过单步扩散模型和调制LoRA方案对3D高斯泼溅视频压缩进行感知增强，在LPIPS和FID指标上显著优于GSVC。
摘要：
3D高斯泼溅（3DGS）通过显式表示和快速渲染增强三维场景重建，展现出对包括视频压缩在内的多种低级视觉任务的潜在优势。然而，现有基于3DGS的视频编解码器通常表现出更明显的视觉伪影和相对较低的压缩比。在本文中，我们专门针对基于3DGS的视频压缩的感知增强问题，基于以下假设：3DGS渲染和量化产生的伪影类似于扩散训练过程中采样的噪声隐变量。基于这一前提，我们提出了一个内容自适应框架GFix，该框架包含一个精简的单步扩散模型，作为现成的神经增强器。此外，为了提高压缩效率，我们提出了一种调制LoRA方案，该方案冻结低秩分解并调制中间隐藏状态，从而以高度可压缩的更新实现扩散主干的高效自适应。实验结果表明，GFix提供了强大的感知质量增强，在LPIPS上相比GSVC实现了高达72.1%的BD-rate节省，在FID上实现了21.4%的BD-rate节省。
英文摘要：
3D Gaussian Splatting (3DGS) enhances 3D scene reconstruction through explicit representation and fast rendering, demonstrating potential benefits for various low-level vision tasks, including video compression. However, existing 3DGS-based video codecs generally exhibit more noticeable visual artifacts and relatively low compression ratios. In this paper, we specifically target the perceptual enhancement of 3DGS-based video compression, based on the assumption that artifacts from 3DGS rendering and quantization resemble noisy latents sampled during diffusion training. Building on this premise, we propose a content-adaptive framework, GFix, comprising a streamlined, single-step diffusion model that serves as an off-the-shelf neural enhancer. Moreover, to increase compression efficiency, We propose a modulated LoRA scheme that freezes the low-rank decompositions and modulates the intermediate hidden states, thereby achieving efficient adaptation of the diffusion backbone with highly compressible updates. Experimental results show that GFix delivers strong perceptual quality enhancement, outperforming GSVC with up to 72.1% BD-rate savings in LPIPS and 21.4% in FID.
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