Generator pyramid for high-resolution image inpainting
Leilei Cao, Tong Yang, Yixu Wang, Bo Yan, Yandong Guo
Abstract
Abstract 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, which explicitly disentangles the task into two sub-tasks: content completion and texture synthesis. PyramidFill attempts to complete the content of unknown regions in a lower-resolution image, and synthesize 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 synthesizing 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.