LightBridge: Feed-Forward Generative Relighting for 3D Gaussian Splatting

2026-09-02

AI Summary

为了解决3D高斯点云渲染结果无法轻易重新打光的问题，提出LightBridge框架，通过前馈生成方式实现高效可控的重新打光。

Abstract

3D Gaussian Splatting (3DGS) achieves high-quality, real-time novel view synthesis, but the resulting assets have baked-in illumination and cannot be easily relit. Inverse rendering methods optimize simplified reflectance and illumination models for each scene, limiting efficiency and relighting quality. Recent generative approaches leverage large diffusion models for realistic lighting edits, but applying them to 3DGS typically requires an additional per-scene optimization stage to bake the edited appearance into the representation. We present LightBridge, a feed-forward generative framework for controllable relighting of complete 3DGS assets in a single pass. To enable feed-forward training, we construct a large-scale Multi-Illumination Relighting Dataset with paired source and target observations of the same scenes. Latent Bridge Relighting Diffusion models relighting as source-to-target transport in latent space, enabling one-step extraction of 2D visual tokens without iterative diffusion sampling. A Gaussian Propagation Transformer uses a point transformer with sparse image-to-point self-attention followed by point-to-image cross-attention to efficiently propagate these cues across the complete 3DGS, while avoiding full attention over all image and Gaussian tokens. Experiments validate these designs, demonstrating competitive relighting quality and efficient single-pass prediction of complete relit 3DGS assets without scene-specific optimization. The code and dataset will be made publicly available upon acceptance.

Problem

• 3D Gaussian Splatting
• Relighting
• Inverse Rendering
• Generative Models
• Diffusion Models

Innovation

• feed-forward generative framework
• Multi-Illumination Relighting Dataset
• Latent Bridge Relighting Diffusion
• Gaussian Propagation Transformer