Paper:
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 1024 × 1024 resolution. A pilot human study shows our approach outperforms state-of-the-art face completion methods. The code will be released upon publication.

Review:
This work uses GANs to recover clean faces from occluded counterparts. The effectiveness of the proposed method is verified qualitatively and quantitatively on CelebA-HQ. The proposed framework can be generalized to several face-related tasks, such as unconstrained face recognition. Although the novelty of the method is not really impressive, the proposed method seems to be useful for face-related applications and the experimental results are convincing to me.
Pros:
- This method is simple, apparently effective and is a nice use of GANs for a practical task. The paper is written clearly and the English is fine.
Cons:
- My main concern with this paper is regarding the novelty. The authors seem to claim a novel GAN architecture by using an adversarial auto-encoder-based architecture. However, it is not clear to me what aspect of their GAN is particularly new.
- Missing experimental comparisons with state-of-the-arts. Detailed experimental comparisons with more state-of-the-arts (e.g., RLA, Zhao et al., TIP 2018, 3D-PIM, Zhao et al., IJCAI 2018) are needed to justify the superiority of the proposed method.
- Missing more in-the-wild comparisons in the Experiment section. This paper mainly performed experiments on CelebA-HQ. More in-the-wild qualitative and quantitative experiments on recent benchmarks with large occlusion variations are needed to verify the efficacy of the proposed method.
Additional comments:
- How did authors update each component and ensure stable yet fast convergence while optimising the whole GAN-based framework? 
- Can the proposed method solve other challenging in-the-wild facial variations except occlusion? e.g., pose, expression, lighting, noise, etc.