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Image harmonization aims at adjusting the appearance of the foreground tomake it more compatible with the background. Without exploring backgroundillumination and its effects on the foreground elements, existing works areincapable of generating a realistic foreground shading. In this paper, wedecompose the image harmonization task into two sub-problems: 1) illuminationestimation of the background image and 2) re-rendering of foreground objectsunder background illumination. Before solving these two sub-problems, we firstlearn a shading-aware illumination descriptor via a well-designed neuralrendering framework, of which the key is a shading bases module that generatesmultiple shading bases from the foreground image. Then we design a backgroundillumination estimation module to extract the illumination descriptor from thebackground. Finally, the Shading-aware Illumination Descriptor is used inconjunction with the neural rendering framework (SIDNet) to produce theharmonized foreground image containing a novel harmonized shading. Moreover, weconstruct a photo-realistic synthetic image harmonization dataset that containsnumerous shading variations with image-based lighting. Extensive experiments onboth synthetic and real data demonstrate the superiority of the proposedmethod, especially in dealing with foreground shadings.

SIDNet: Learning Shading-aware Illumination Descriptor for Image Harmonization