  Recent advances in deep generative models have shown promising potential in
image inpanting, which refers to the task of predicting missing pixel values of
an incomplete image using the known context. However, existing methods can be
slow or generate unsatisfying results with easily detectable flaws. In
addition, there is often perceivable discontinuity near the holes and require
further post-processing to blend the results. We present a new approach to
address the difficulty of training a very deep generative model to synthesize
high-quality photo-realistic inpainting. Our model uses conditional generative
adversarial networks (conditional GANs) as the backbone, and we introduce a
novel block-wise procedural training scheme to stabilize the training while we
increase the network depth. We also propose a new strategy called adversarial
loss annealing to reduce the artifacts. We further describe several losses
specifically designed for inpainting and show their effectiveness. Extensive
experiments and user-study show that our approach outperforms existing methods
in several tasks such as inpainting, face completion and image harmonization.
Finally, we show our framework can be easily used as a tool for interactive
guided inpainting, demonstrating its practical value to solve common real-world
challenges.
