TFill: Image Completion via a Transformer-Based Architecture
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Bridging distant context interactions is important for high quality 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 treating image completion as a directionless sequence-to-sequence prediction task, and deploy a transformer to directly capture long-range dependence in the encoder in a first phase. Crucially, we employ a restrictive CNN with small and non-overlapping RF for token representation, which allows the transformer to explicitly model the long-range context relations with equal importance in all layers, without implicitly confounding neighboring tokens when larger RFs are used. In a second phase, to improve appearance consistency between visible and generated regions, a novel attention-aware layer (AAL) is introduced to better exploit distantly related features and also avoid the insular effect of standard attention. Overall, extensive experiments demonstrate superior performance compared to state-of-the-art methods on several datasets.
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