A Face Inpainting Algorithm with Local Attribute Generative Adversarial Networks

Recent researches in neural network models have shown great potential in image inpainting task, which focuses on understanding image semantic information and reconstructs missing image content. These researches can generate visually reasonable image structures and textures, however, they usually produce distorted structures or blurry textures that are inconsistent with the surrounding areas, especially for the face inpainting task. The face inpainting task is often necessary to gene the advantages of fully connected convolution and U-net network, and the model proposes locally attributes discriminator to make the inpainted contents more innovative and is able to keep the global and local semantic consistency. The model not only improves the perception of the overall semantic information of the face image, but also restores the key parts of the face based on the local attributes. Experiments on the CelebA dataset have shown that our model can effectively deal with face image repair problems and generate novel results.