Semantic image inpainting is a challenging task where large missing regions
have to be filled based on the available visual data. Existing methods which
extract information from only a single image generally produce unsatisfactory
results due to the lack of high level context. In this paper, we propose a
novel method for semantic image inpainting, which generates the missing content
by conditioning on the available data. Given a trained generative model, we
search for the closest encoding of the corrupted image in the latent image
manifold using our context and prior losses. This encoding is then passed
through the generative model to infer the missing content. In our method,
inference is possible irrespective of how the missing content is structured,
while the state-of-the-art learning based method requires specific information
about the holes in the training phase. Experiments on three datasets show that
our method successfully predicts information in large missing regions and
achieves pixel-level photorealism, significantly outperforming the
state-of-the-art methods.

本文提出了一种基于生成模型的新型语义图像修复方法，该方法可在缺失的内容相互独立的情况下进行推理，并取得超越其他方法的像素级真实感。