  Image inpainting has achieved remarkable progress and inspired abundant
methods, where the critical bottleneck is identified as how to fulfill the
high-frequency structure and low-frequency texture information on the masked
regions with semantics. To this end, deep models exhibit powerful superiority
to capture them, yet constrained on the local spatial regions. In this paper,
we delve globally into texture and structure information to well capture the
semantics for image inpainting. As opposed to the existing arts trapped on the
independent local patches, the texture information of each patch is
reconstructed from all other patches across the whole image, to match the
coarsely filled information, specially the structure information over the
masked regions. Unlike the current decoder-only transformer within the pixel
level for image inpainting, our model adopts the transformer pipeline paired
with both encoder and decoder. On one hand, the encoder captures the texture
semantic correlations of all patches across image via self-attention module. On
the other hand, an adaptive patch vocabulary is dynamically established in the
decoder for the filled patches over the masked regions. Building on this, a
structure-texture matching attention module anchored on the known regions comes
up to marry the best of these two worlds for progressive inpainting via a
probabilistic diffusion process. Our model is orthogonal to the fashionable
arts, such as Convolutional Neural Networks (CNNs), Attention and Transformer
model, from the perspective of texture and structure information for image
inpainting. The extensive experiments over the benchmarks validate its
superiority. Our code is available at
https://github.com/htyjers/DGTS-Inpainting.
