Image Inpainting using Block-wise Procedural Training with Annealed Adversarial Counterpart
This work addresses image inpainting challenges for computer vision applications, offering incremental improvements over prior methods.
The paper tackles the problem of slow and flawed image inpainting in deep generative models by introducing a block-wise procedural training scheme and adversarial loss annealing, resulting in outperforming existing methods in tasks like inpainting and face completion as shown in experiments and user studies.
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.