GaussVid: Sparse-View Gaussian Splatting with 3D-Aware Video Diffusion Priors

2026-08-22

AI Summary

本文提出了一种3D感知视频恢复框架，通过引入相机条件几何先验来解决稀疏视图下3D高斯点云重建存在的多视角不一致问题。

Abstract

3D Gaussian Splatting (3DGS) has achieved remarkable success in novel view synthesis; however, reconstructions under sparse views often exhibit noticeable artifacts. While recent video diffusion models provide strong spatio-temporal priors for 3DGS restoration, directly fine-tuning them for restoration is suboptimal, as they lack awareness of the underlying multi-camera geometry, resulting in multi-view inconsistencies. In this work, we propose a novel 3D-aware video restoration framework designed to enhance the quality of sparse 3DGS reconstruction. Specifically, we construct a large-scale 3DGS video dataset to enable specialized fine-tuning. To bridge the gap between 2D video generation and 3D multi-view constraints, we introduce a camera-conditioned geometric prior. By using the first and last frames as boundary anchors and encoding the corresponding camera relationships, we explicitly inject spatial structure into the video generation pipeline. This boundary-anchored, camera-aware prior guides the network toward geometrically grounded restoration that remains coherent across viewpoints. Extensive experiments show that, among video-prior restoration methods, our approach attains the best pixel- and structure-level fidelity (PSNR/SSIM) and improves multi-view consistency, while remaining competitive in perceptual quality (LPIPS).

Problem

• Sparse-View
• Gaussian Splatting
• 3D-Aware
• Video Diffusion Priors
• Multi-View Inconsistencies

Innovation

• 3D-aware video restoration
• camera-conditioned geometric prior
• sparse 3DGS reconstruction
• boundary-anchored prior