  State-of-the-art image inpainting approaches can suffer from generating
distorted structures and blurry textures in high-resolution images (e.g.,
512x512). The challenges mainly drive from (1) image content reasoning from
distant contexts, and (2) fine-grained texture synthesis for a large missing
region. To overcome these two challenges, we propose an enhanced GAN-based
model, named Aggregated COntextual-Transformation GAN (AOT-GAN), for
high-resolution image inpainting. Specifically, to enhance context reasoning,
we construct the generator of AOT-GAN by stacking multiple layers of a proposed
AOT block. The AOT blocks aggregate contextual transformations from various
receptive fields, allowing to capture both informative distant image contexts
and rich patterns of interest for context reasoning. For improving texture
synthesis, we enhance the discriminator of AOT-GAN by training it with a
tailored mask-prediction task. Such a training objective forces the
discriminator to distinguish the detailed appearances of real and synthesized
patches, and in turn, facilitates the generator to synthesize clear textures.
Extensive comparisons on Places2, the most challenging benchmark with 1.8
million high-resolution images of 365 complex scenes, show that our model
outperforms the state-of-the-art by a significant margin in terms of FID with
38.60% relative improvement. A user study including more than 30 subjects
further validates the superiority of AOT-GAN. We further evaluate the proposed
AOT-GAN in practical applications, e.g., logo removal, face editing, and object
removal. Results show that our model achieves promising completions in the real
world. We release code and models in
https://github.com/researchmm/AOT-GAN-for-Inpainting.
