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.
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.