  Prior knowledge of face shape and structure plays an important role in face
inpainting. However, traditional face inpainting methods mainly focus on the
generated image resolution of the missing portion without consideration of the
special particularities of the human face explicitly and generally produce
discordant facial parts. To solve this problem, we present a domain embedded
multi-model generative adversarial model for inpainting of face images with
large cropped regions. We firstly represent only face regions using the latent
variable as the domain knowledge and combine it with the non-face parts
textures to generate high-quality face images with plausible contents. Two
adversarial discriminators are finally used to judge whether the generated
distribution is close to the real distribution or not. It can not only
synthesize novel image structures but also explicitly utilize the embedded face
domain knowledge to generate better predictions with consistency on structures
and appearance. Experiments on both CelebA and CelebA-HQ face datasets
demonstrate that our proposed approach achieved state-of-the-art performance
and generates higher quality inpainting results than existing ones.
