空間制御可能な多視点屋内シーン再照明のための分離照明事前分布
Decoupled Illumination Priors for Spatially Controllable Multi-View Indoor Scene Relighting
屋内シーンの再照明を、事前学習済み拡散モデルから照明パレットを抽出する段階と、粗い3D形状から目標照明条件を明示的にマッピングする段階に分離し、空間制御と多視点一貫性を両立するフレームワークを提案した。
著者: Chenjian Gao, Linning Xu, Tianfan Xue
分類: cs.CV
原文アブストラクト
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: https://cjeen.github.io/lumepalette