Dior: Drawing the Light of Image via Material-Decoupled Illumination Representation

2026-08-30

AI Summary

本文通过引入材料解耦的光照表示法Lumi Map，解决了手绘草图控制图像重新打光时精度和可控性不足的问题。

Abstract

Controllable image relighting is an important problem in image editing, and hand-drawn scribbles provide an intuitive interface for specifying the desired illumination. However, existing methods do not establish a consistent and effective mapping between scribble inputs and relighting results, limiting their ability to control illumination intensity, chromaticity, and complex spatial distributions. We address this limitation by introducing a material-decoupled illumination representation, termed the Lumi Map, which establishes an explicit mapping between user scribbles and the resulting illumination, thereby improving both relighting accuracy and controllability. Specifically, we use a renderer to synthesize source image-Lumi Map-relit image triplets and train the model to predict the target relighting result conditioned on the Lumi Map. To mitigate the domain gap introduced by synthetic data, we further perform reconstruction training on real relighting pairs, improving the model's generalization to real-world images. Finally, we present Dior-Light, an image relighting method controlled by hand-drawn strokes. Extensive experiments demonstrate that our method outperforms existing approaches in relighting accuracy and enables effective control over illumination intensity and chromaticity on in-the-wild images.

Problem

• controllable image relighting
• hand-drawn scribbles
• illumination intensity
• chromaticity

Innovation

• material-decoupled illumination
• Lumi Map
• hand-drawn scribbles
• reconstruction training
• image relighting