  Recent deep generative models have achieved promising performance in image
inpainting. However, it is still very challenging for a neural network to
generate realistic image details and textures, due to its inherent spectral
bias. By our understanding of how artists work, we suggest to adopt a
`structure first detail next' workflow for image inpainting. To this end, we
propose to build a Pyramid Generator by stacking several sub-generators, where
lower-layer sub-generators focus on restoring image structures while the
higher-layer sub-generators emphasize image details. Given an input image, it
will be gradually restored by going through the entire pyramid in a bottom-up
fashion. Particularly, our approach has a learning scheme of progressively
increasing hole size, which allows it to restore large-hole images. In
addition, our method could fully exploit the benefits of learning with
high-resolution images, and hence is suitable for high-resolution image
inpainting. Extensive experimental results on benchmark datasets have validated
the effectiveness of our approach compared with state-of-the-art methods.
