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Recent advances in deep generative models have shown promising potential inimage inpanting, which refers to the task of predicting missing pixel values ofan incomplete image using the known context. However, existing methods can beslow or generate unsatisfying results with easily detectable flaws. Inaddition, there is often perceivable discontinuity near the holes and requirefurther post-processing to blend the results. We present a new approach toaddress the difficulty of training a very deep generative model to synthesizehigh-quality photo-realistic inpainting. Our model uses conditional generativeadversarial networks (conditional GANs) as the backbone, and we introduce anovel block-wise procedural training scheme to stabilize the training while weincrease the network depth. We also propose a new strategy called adversarialloss annealing to reduce the artifacts. We further describe several lossesspecifically designed for inpainting and show their effectiveness. Extensiveexperiments and user-study show that our approach outperforms existing methodsin several tasks such as inpainting, face completion and image harmonization.Finally, we show our framework can be easily used as a tool for interactiveguided inpainting, demonstrating its practical value to solve common real-worldchallenges.

Image Inpainting using Block-wise Procedural Training with Annealed Adversarial Counterpart