让室内场景光照可精准控制且多视角一致，还保持真实材质效果。
Decoupled Illumination Priors for Spatially Controllable Multi-View Indoor Scene Relighting
- 分两阶段处理：先提取真实光照调色板，再根据3D结构投射目标光照
- 在合成与真实场景上均实现逼真、可控、多视角一致的重光照效果
- 适合需要精细光照调控的3D内容生成、虚拟拍摄等应用
室内场景重光照要求高度逼真、精确的空间控制和严格的多视角一致性。尽管基于扩散模型的图像编辑可通过文本提示实现语义光照操控，但精确的3D光照定位常破坏其生成先验。我们提出Lume-Palette，一种渐进式框架，利用语义光照先验实现空间可控的多视角室内重光照。该方法将重光照分为两个阶段：(1) 光照提炼，从预训练扩散模型中提取标准光照调色板以保留真实材质-光照交互；(2) 光照投射，基于粗略3D几何显式映射目标空间光照条件。为高效处理密集多视角与多模态输入，引入非对称多视角条件策略，选择性注入关键空间上下文。在多种合成与真实场景上的实验表明，Lume-Palette能生成逼真、空间可控且多视角一致的重光照结果。
原文摘要 · Abstract (English)
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
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