[Submitted on 9 Jul 2026]
Title:Decoupled Illumination Priors for Spatially Controllable Multi-View Indoor Scene Relighting
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Abstract:Indoor scene relighting demands photorealism, precise spatial control, and strict multi-view consistency. While diffusion-based image editing models enable semantic lighting manipulation via text prompts, enforcing exact 3D light placement often disrupts their generative priors. We propose Lume-Palette, a progressive framework that leverages semantic lighting priors for spatially controllable multi-view indoor relighting. The approach decouples relighting into two stages: (1) illumination distillation, which extracts canonical illumination palettes from a pretrained diffusion model to preserve realistic material-light interactions, and (2) illumination casting, which explicitly maps target spatial lighting conditions defined from coarse 3D geometry. To efficiently handle dense multi-view and multi-modal inputs, we introduce an asymmetric multi-view conditioning strategy that selectively injects essential spatial context. Experiments on diverse synthetic scenes and real-world scenes demonstrate that Lume-Palette produces photorealistic, spatially controllable, and multi-view consistent relighting results. Project Page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.08879 [cs.CV]
(or arXiv:2607.08879v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.08879
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Chenjian Gao [view email] [v1] Thu, 9 Jul 2026 19:09:48 UTC (4,297 KB)
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