Face Image Completion Method Based on Parsing Features Maps

Abstract

In this paper, we focus on the facial completion task based on the parsing feature map. In recent years, methods based on deep learning have achieved remarkable results in face image completion. However, since many methods do not consider the semantic structure information of face images, these methods may lead to unreasonable or discontinuous cases in the return results. To solve this problem, we propose a face image completion network based on face parsing features. The network includes two sub-network: face parsing map prediction network and face image completion network. The face parsing map prediction network predicts the parsing features of the missing regions, and the face image completion network uses these features to guide the face image completion network to fill the missing areas. On the open dataset, CelebA-HQ and Flickr Faces High Quality(FFHQ), the validity of this method is verified by qualitative and quantitative analysis.