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We present a deep learning approach for high resolution face completion withmultiple controllable attributes (e.g., male and smiling) under arbitrarymasks. Face completion entails understanding both structural meaningfulness andappearance consistency locally and globally to fill in "holes" whose content donot appear elsewhere in an input image. It is a challenging task with thedifficulty level increasing significantly with respect to high resolution, thecomplexity of "holes" and the controllable attributes of filled-in fragments.Our system addresses the challenges by learning a fully end-to-end frameworkthat trains generative adversarial networks (GANs) progressively from lowresolution to high resolution with conditional vectors encoding controllableattributes.  We design novel network architectures to exploit information across multiplescales effectively and efficiently. We introduce new loss functions encouragingsharp completion. We show that our system can complete faces with largestructural and appearance variations using a single feed-forward pass ofcomputation with mean inference time of 0.007 seconds for images at 1024 x 1024resolution. We also perform a pilot human study that shows our approachoutperforms state-of-the-art face completion methods in terms of rank analysis.The code will be released upon publication.

High Resolution Face Completion with Multiple Controllable Attributes via Fully End-to-End Progressive Generative Adversarial Networks