  Inpainting high-resolution images with large holes challenges existing deep
learning based image inpainting methods. We present a novel framework --
PyramidFill for high-resolution image inpainting task, which explicitly
disentangles content completion and texture synthesis. PyramidFill attempts to
complete the content of unknown regions in a lower-resolution image, and
synthesis the textures of unknown regions in a higher-resolution image,
progressively. Thus, our model consists of a pyramid of fully convolutional
GANs, wherein the content GAN is responsible for completing contents in the
lowest-resolution masked image, and each texture GAN is responsible for
synthesizing textures in a higher-resolution image. Since completing contents
and synthesising textures demand different abilities from generators, we
customize different architectures for the content GAN and texture GAN.
Experiments on multiple datasets including CelebA-HQ, Places2 and a new natural
scenery dataset (NSHQ) with different resolutions demonstrate that PyramidFill
generates higher-quality inpainting results than the state-of-the-art methods.
To better assess high-resolution image inpainting methods, we will release
NSHQ, high-quality natural scenery images with high-resolution
1920$\times$1080.
