Face completion is a challenging conditional image synthesis task. This paper proposes controllable and interpretable high-resolution and fast face completion by learning generative adversarial networks (GANs) progressively from low resolution to high resolution. We present structure-aware and frequency-oriented attentive GANs. The proposed structure-aware component leverages off-the-shelf facial landmark detectors and proposes a simple yet effective method of integrating the detected landmarks in generative learning. It facilitates facial expression transfer together with facial attributes control, and helps regularize the structural consistency in progressive training. The proposed frequency-oriented attentive module (FOAM) encourages GANs to attend much more to finer details in the coarse-to-fine progressive training, thus enabling progressive attention to face structures. The learned FOAMs show a strong pattern of switching their attention from low-frequency to high-frequency signals. In experiments, the proposed method is tested on the CelebA-HQ benchmark. Experiment results show that our approach outperforms state-of-the-art face completion methods. The proposed method is also fast with mean inference time of 0.54 seconds for images at 1024 × 1024 resolution (using a Titan Xp GPU).
This paper proposes controllable and interpretable high-resolution and fast face completion by learning generative adversarial networks (GANs) progressively from low resolution to high resolution. It combines the masks, landmarks, corrupted images as inputs to generate completed images in high-resolution. The proposed frequency-oriented attentive module (FOAM) encourages GANs to highlight much more to finer details in the coarse-to-fine progressive training, thus enabling progressive attention to face structures.
Integrating the Progressive growing GAN (PGGAN) for high-resolution face completion is an interesting step-up work after the success of PGGAN on high-resolution image generation.
Basically the FOAM is proposed to merge the images from different resolution levels instead of a weighted summation. The novelty, however, is not strong as the simple summation provides reasonable performance as convinced in the progressive growing GAN paper. And there lacks quantitative analysis on ablation study as showed in Figure 4. Therefore, the contributions of each component are not convincing.
Using Progressive Growing GAN for high-resolution face completion is also studied in [1]. Figure 17, Figure 18, indicates the author used the same structure as used by [1]. However,  [1] is not cited and there is no comparison between these two models in the paper.
[1] Zeyuan Liu, et al  "High Resolution Face Completion with Multiple Controllable Attributes via Fully End-to-End Progressive Generative Adversarial Networks "