High Resolution and Fast Face Completion via Progressively Attentive GANs

Zeyuan Chen, Shaoliang Nie, Tianfu Wu, Christopher G. Healey

Abstract: Face completion is a challenging task with the difficulty level increasing significantly with respect to high resolution, the complexity of "holes" and the controllable attributes of filled-in fragments. Our system addresses the challenges by learning a fully end-to-end framework that trains generative adversarial networks (GANs) progressively from low resolution to high resolution with conditional vectors encoding controllable attributes. We design a novel coarse-to-fine attentive module network architecture. Our model is encouraged to attend on finer details while the network is growing to a higher resolution, thus being capable of showing progressive attention to different frequency components in a coarse-to-fine way. We term the module Frequency-oriented Attentive Module (FAM). Our system can complete faces with large structural and appearance variations using a single feed-forward pass of computation with mean inference time of 0.54 seconds for images at 1024x1024 resolution. A pilot human study shows our approach outperforms state-of-the-art face completion methods. The code will be released upon publication.

Keywords: Face Completion, progressive GANs, Attribute Control, Frequency-oriented Attention

Data: [CelebA-HQ](https://paperswithcode.com/dataset/celeba-hq)