Decoupled Illumination Priors for Spatially Controllable Multi-View Indoor Scene Relighting

Indoor scene relighting demands photorealism, precise spa-tial control, and strict multi-view consistency. While diffusion-based im-age editing models enable semantic lighting manipulation via text prompts,enforcing exact 3D light placement often disrupts their generative pri-ors. We propose Lume-Palette, a progressive framework that leveragessemantic lighting priors for spatially controllable multi-view indoor re-lighting. The approach decouples relighting into two stages: (1) illumi-nation distillation, which extracts canonical illumination palettes froma pretrained diffusion model to preserve realistic material–light interac-tions, and (2) illumination casting, which explicitly maps target spatiallighting conditions defined from coarse 3D geometry. To efficiently handledense multi-view and multi-modal inputs, we introduce an asymmetricmulti-view conditioning strategy that selectively injects essential spatialcontext. Experiments on diverse synthetic scenes and real-world scenesdemonstrate that Lume-Palette produces photorealistic, spatially con-trollable, and multi-view consistent relighting results.