  Image completion has made tremendous progress with convolutional neural
networks (CNNs), because of their powerful texture modeling capacity. However,
due to some inherent properties (e.g., local inductive prior, spatial-invariant
kernels), CNNs do not perform well in understanding global structures or
naturally support pluralistic completion. Recently, transformers demonstrate
their power in modeling the long-term relationship and generating diverse
results, but their computation complexity is quadratic to input length, thus
hampering the application in processing high-resolution images. This paper
brings the best of both worlds to pluralistic image completion: appearance
prior reconstruction with transformer and texture replenishment with CNN. The
former transformer recovers pluralistic coherent structures together with some
coarse textures, while the latter CNN enhances the local texture details of
coarse priors guided by the high-resolution masked images. The proposed method
vastly outperforms state-of-the-art methods in terms of three aspects: 1) large
performance boost on image fidelity even compared to deterministic completion
methods; 2) better diversity and higher fidelity for pluralistic completion; 3)
exceptional generalization ability on large masks and generic dataset, like
ImageNet.
