Facial image inpainting is a task of filling visually realistic and semantically meaningful contents for missing or masked pixels in a face image. Although existing methods have made significant progress in achieving high visual quality, the controllable diversity of facial image inpainting remains an open problem in this field. This paper introduces EXE-GAN, a novel diverse and interactive facial inpainting framework, which can not only preserve the high-quality visual effect of the whole image but also complete the face image with exemplar-like facial attributes. The proposed facial inpainting is achieved based on generative adversarial networks by leveraging the global style of input image, the stochastic style, and the exemplar style of example image. A novel attribute similarity metric is introduced to encourage networks to learn the style of facial attributes from the exemplar in a self-supervised way. To guarantee the natural transition across the boundary of inpainted regions, a novel spatial variant gradient backpropagation technique is designed to adjust the loss gradients based on the spatial location. A variety of experimental results and comparisons on public CelebA-HQ and FFHQ datasets are presented to demonstrate the superiority of the proposed method in terms of both the quality and diversity in facial inpainting.

翻译：显性图像油漆是一项任务,要填充在脸部图像中丢失或遮盖像像素的视觉现实和语义上有意义的内容。虽然现有方法在实现高视觉质量方面已取得重大进展,但面部图像油漆的可控制多样性仍然是该领域的一个未解决的问题。本文介绍EXE-GAN,这是一个全新的多样化和互动面部涂色框架,它不仅能够保存整个图像的高质量视觉效果,而且能够用像样面部面部特征完成脸部图像的自然转换。拟议的面部涂色是根据基因式对立网络实现的,其方法是利用输入图像的全球风格、沙眼风格和示例图像的外观风格。采用了一种新颖的属性相似性衡量标准,鼓励网络以自我监督的方式学习Exmparm的面部特征的风格。为了保证整个图像区域边界的自然转变,一个新的空间变异性梯度反演化技术旨在调整以空间位置为基础的损失梯度。 各种实验结果和图像多样性的对比,展示了公共质量的CalebA-Q的高级性数据。