  Bridging global context interactions correctly is important for high-fidelity
image completion with large masks. Previous methods attempting this via deep or
large receptive field (RF) convolutions cannot escape from the dominance of
nearby interactions, which may be inferior. In this paper, we propose to treat
image completion as a directionless sequence-to-sequence prediction task, and
deploy a transformer to directly capture long-range dependence in the encoder.
Crucially, we employ a restrictive CNN with small and non-overlapping RF for
weighted token representation, which allows the transformer to explicitly model
the long-range visible context relations with equal importance in all layers,
without implicitly confounding neighboring tokens when larger RFs are used. To
improve appearance consistency between visible and generated regions, a novel
attention-aware layer (AAL) is introduced to better exploit distantly related
high-frequency features. Overall, extensive experiments demonstrate superior
performance compared to state-of-the-art methods on several datasets.
