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Prior knowledge of face shape and structure plays an important role in faceinpainting. However, traditional face inpainting methods mainly focus on thegenerated image resolution of the missing portion without consideration of thespecial particularities of the human face explicitly and generally producediscordant facial parts. To solve this problem, we present a domain embeddedmulti-model generative adversarial model for inpainting of face images withlarge cropped regions. We firstly represent only face regions using the latentvariable as the domain knowledge and combine it with the non-face partstextures to generate high-quality face images with plausible contents. Twoadversarial discriminators are finally used to judge whether the generateddistribution is close to the real distribution or not. It can not onlysynthesize novel image structures but also explicitly utilize the embedded facedomain knowledge to generate better predictions with consistency on structuresand appearance. Experiments on both CelebA and CelebA-HQ face datasetsdemonstrate that our proposed approach achieved state-of-the-art performanceand generates higher quality inpainting results than existing ones.

Domain Embedded Multi-model Generative Adversarial Networks for Image-based Face Inpainting