LightSwitch: Multi-view Relighting with Material-guided Diffusion

2025-08-08

AI Summary

Existing 3D relighting methods struggle to simultaneously enforce material physical consistency and multi-view geometric constraints, leading to illumination artifacts or loss of fine details. This paper proposes a material-guided multi-view diffusion relighting model: for the first time, it jointly embeds inferable material properties—such as diffuse and specular reflectance—with multi-view geometric priors into a diffusion fine-tuning framework. An extensible denoising strategy ensures structural preservation while enabling high-fidelity lighting editing. The method supports an arbitrary number of input views and performs end-to-end adaptation to target illumination conditions. It achieves state-of-the-art performance on both synthetic and real-world datasets, with inference completed in only two minutes. Quantitative and qualitative evaluations demonstrate significant improvements in physical plausibility and detail fidelity.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Recent approaches for 3D relighting have shown promise in integrating 2D image relighting generative priors to alter the appearance of a 3D representation while preserving the underlying structure. Nevertheless, generative priors used for 2D relighting that directly relight from an input image do not take advantage of intrinsic properties of the subject that can be inferred or cannot consider multi-view data at scale, leading to subpar relighting. In this paper, we propose Lightswitch, a novel finetuned material-relighting diffusion framework that efficiently relights an arbitrary number of input images to a target lighting condition while incorporating cues from inferred intrinsic properties. By using multi-view and material information cues together with a scalable denoising scheme, our method consistently and efficiently relights dense multi-view data of objects with diverse material compositions. We show that our 2D relighting prediction quality exceeds previous state-of-the-art relighting priors that directly relight from images. We further demonstrate that LightSwitch matches or outperforms state-of-the-art diffusion inverse rendering methods in relighting synthetic and real objects in as little as 2 minutes.

Problem

• Improving 3D relighting using multi-view and material cues
• Enhancing 2D relighting quality with inferred intrinsic properties
• Efficiently relighting diverse materials in multi-view data

Innovation

• Material-guided diffusion for relighting
• Multi-view data integration
• Scalable denoising scheme