LuxRemix: Lighting Decomposition and Remixing for Indoor Scenes
Ruofan Liang, Norman Müller, Ethan Weber, Duncan Zauss, Nandita Vijaykumar, Peter Kontschieder, Christian Richardt
TL;DR
LuxRemix addresses the challenge of post-capture lighting control in indoor scenes by integrating a single-image OLAT lighting decomposition with multi-view harmonization and a real-time relightable 3D Gaussian splatting representation. The method enables independent manipulation of individual near-field light sources (on/off, color, intensity) while ensuring consistency across views and enabling real-time interactive relighting from novel viewpoints. It introduces a three-stage pipeline: synthetic dataset generation for per-light decomposition, cross-view harmonization with Plücker ray embeddings, and per-light RGB parameter fitting on a 3D Gaussian splatting backbone, achieving high-fidelity, view-consistent relighting. This work advances photorealistic indoor relighting with fine-grained light control, with potential impact on photography, cinematography, and virtual production, while noting limitations in generalization to dynamic outdoor scenes and absence of distant HDRI editing.
Abstract
We present a novel approach for interactive light editing in indoor scenes from a single multi-view scene capture. Our method leverages a generative image-based light decomposition model that factorizes complex indoor scene illumination into its constituent light sources. This factorization enables independent manipulation of individual light sources, specifically allowing control over their state (on/off), chromaticity, and intensity. We further introduce multi-view lighting harmonization to ensure consistent propagation of the lighting decomposition across all scene views. This is integrated into a relightable 3D Gaussian splatting representation, providing real-time interactive control over the individual light sources. Our results demonstrate highly photorealistic lighting decomposition and relighting outcomes across diverse indoor scenes. We evaluate our method on both synthetic and real-world datasets and provide a quantitative and qualitative comparison to state-of-the-art techniques. For video results and interactive demos, see https://luxremix.github.io.
