  We present a deep learning approach for high resolution face completion with
multiple controllable attributes (e.g., male and smiling) under arbitrary
masks. Face completion entails understanding both structural meaningfulness and
appearance consistency locally and globally to fill in "holes" whose content do
not appear elsewhere in an input image. It 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 novel network architectures to exploit information across multiple
scales effectively and efficiently. We introduce new loss functions encouraging
sharp completion. We show that our system can complete faces with large
structural and appearance variations using a single feed-forward pass of
computation with mean inference time of 0.007 seconds for images at 1024 x 1024
resolution. We also perform a pilot human study that shows our approach
outperforms state-of-the-art face completion methods in terms of rank analysis.
The code will be released upon publication.
