发表机构
Multimedia Laboratory, The Chinese University of Hong Kong; Shanghai AI Laboratory; CPII under InnoHK（香港中文大学多媒体实验室; 上海人工智能实验室; 创新香港研发平台下的CPII）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究室内场景重光照问题，提出Lume-Palette框架，通过将重光照解耦为光照蒸馏和投射两阶段，并引入不对称多视图条件策略，实现了逼真、空间可控且多视图一致的重光照效果。
AI 中文摘要
室内场景重光照需要照片般的真实感、精确的空间控制和严格的多视图一致性。虽然基于扩散的图像编辑模型能够通过文本提示进行语义光照操作，但强制精确的3D光照放置往往会破坏其生成先验。我们提出了Lume-Palette，这是一个渐进式框架，利用语义光照先验进行空间可控的多视图室内重光照。该方法将重光照解耦为两个阶段：（1）光照蒸馏，从预训练的扩散模型中提取规范光照调色板以保留真实的材质-光照交互；（2）光照投射，明确映射从粗3D几何定义的目标空间光照条件。为有效处理密集的多视图和多模态输入，引入了一种不对称多视图条件策略，有选择地注入基本空间上下文。在不同合成场景和真实世界场景上的实验表明，Lume-Palette产生了逼真、空间可控且多视图一致的重光照结果。
英文摘要
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
