Image harmonization aims at adjusting the appearance of the foreground to make it more compatible with the background. Due to a lack of understanding of the background illumination direction, existing works are incapable of generating a realistic foreground shading. In this paper, we decompose the image harmonization into two sub-problems: 1) illumination estimation of background images and 2) rendering of foreground objects. Before solving these two sub-problems, we first learn a direction-aware illumination descriptor via a neural rendering framework, of which the key is a Shading Module that decomposes the shading field into multiple shading components given depth information. Then we design a Background Illumination Estimation Module to extract the direction-aware illumination descriptor from the background. Finally, the illumination descriptor is used in conjunction with the neural rendering framework to generate the harmonized foreground image containing a novel harmonized shading. Moreover, we construct a photo-realistic synthetic image harmonization dataset that contains numerous shading variations by image-based lighting. Extensive experiments on this dataset demonstrate the effectiveness of the proposed method. Our dataset and code will be made publicly available.

翻译：图像统一的目的是调整前景的外观, 使其更符合背景。 由于对背景光照方向缺乏了解, 现有的作品无法生成现实的前景阴影。 在本文中, 我们将图像统一化分为两个子问题:(1) 背景图像的光化估计和(2) 前景对象的映射。 在解决这两个子问题之前, 我们首先通过一个神经构建框架学习一个有方向感知的浅色描述符, 其中的关键是一个将阴影字段分解成多个阴影部分的阴影模组, 并获得深度信息 。 然后我们设计一个背景光化激励模组, 从背景中提取有方向觉识的浅色描述符。 最后, 光化描述符与神经构建框架结合使用, 以生成包含新式统一阴影的地面图像描述框架。 此外, 我们构建了一个光化成图像的图像合成图像集集集集, 包含大量阴影变异的图像化数据, 将展示我们提出的数据定义的有效性 。