When Face Completion Meets Irregular Holes: An Attributes Guided Deep Inpainting Network

Lots of convolutional neural network (CNN)-based methods have been proposed to implement face completion with regular holes. However, in practical applications, irregular holes are more common to see. Moreover, due to the distinct attributes and large variation of appearance for human faces, it is more challenging to fill irregular holes in face images while keeping content consistent with the rest region. Since facial attributes (e.g., gender, smiling, pointy nose, etc.) allow for a more understandable description of one face, they can provide some hints that benefit the face completion task. In this work, we propose a novel attributes-guided face completion network (AttrFaceNet), which comprises a facial attribute prediction subnet and a face completion subnet. The attribute prediction subnet predicts facial attributes from the rest parts of the corrupted images and guides the face completion subnet to fill the missing regions. The proposed AttrFaceNet is evaluated in an end-to-end way on commonly used datasets CelebA and Helen. Extensive experimental results show that our method outperforms state-of-the-art methods qualitatively and quantitatively especially in large mask size cases. Code is available at https://github.com/FVL2020/AttrFaceNet.