  Deep image inpainting has made impressive progress with recent advances in
image generation and processing algorithms. We claim that the performance of
inpainting algorithms can be better judged by the generated structures and
textures. Structures refer to the generated object boundary or novel geometric
structures within the hole, while texture refers to high-frequency details,
especially man-made repeating patterns filled inside the structural regions. We
believe that better structures are usually obtained from a coarse-to-fine
GAN-based generator network while repeating patterns nowadays can be better
modeled using state-of-the-art high-frequency fast fourier convolutional
layers. In this paper, we propose a novel inpainting network combining the
advantages of the two designs. Therefore, our model achieves a remarkable
visual quality to match state-of-the-art performance in both structure
generation and repeating texture synthesis using a single network. Extensive
experiments demonstrate the effectiveness of the method, and our conclusions
further highlight the two critical factors of image inpainting quality,
structures, and textures, as the future design directions of inpainting
networks.
