  Recovering badly damaged face images is a useful yet challenging task,
especially in extreme cases where the masked or damaged region is very large.
One of the major challenges is the ability of the system to generalize on faces
outside the training dataset. We propose to tackle this extreme inpainting task
with a conditional Generative Adversarial Network (GAN) that utilizes
structural information, such as edges, as a prior condition. Edge information
can be obtained from the partially masked image and a structurally similar
image or a hand drawing. In our proposed conditional GAN, we pass the
conditional input in every layer of the encoder while maintaining consistency
in the distributions between the learned weights and the incoming conditional
input. We demonstrate the effectiveness of our method with badly damaged face
examples.
