  Recent advances in image inpainting have shown impressive results for
generating plausible visual details on rather simple backgrounds. However, for
complex scenes, it is still challenging to restore reasonable contents as the
contextual information within the missing regions tends to be ambiguous. To
tackle this problem, we introduce pretext tasks that are semantically
meaningful to estimating the missing contents. In particular, we perform
knowledge distillation on pretext models and adapt the features to image
inpainting. The learned semantic priors ought to be partially invariant between
the high-level pretext task and low-level image inpainting, which not only help
to understand the global context but also provide structural guidance for the
restoration of local textures. Based on the semantic priors, we further propose
a context-aware image inpainting model, which adaptively integrates global
semantics and local features in a unified image generator. The semantic learner
and the image generator are trained in an end-to-end manner. We name the model
SPL to highlight its ability to learn and leverage semantic priors. It achieves
the state of the art on Places2, CelebA, and Paris StreetView datasets.
