Transformer, as a strong and flexible architecture for modelling long-range
relations, has been widely explored in vision tasks. However, when used in
video inpainting that requires fine-grained representation, existed method
still suffers from yielding blurry edges in detail due to the hard patch
splitting. Here we aim to tackle this problem by proposing FuseFormer, a
Transformer model designed for video inpainting via fine-grained feature fusion
based on novel Soft Split and Soft Composition operations. The soft split
divides feature map into many patches with given overlapping interval. On the
contrary, the soft composition operates by stitching different patches into a
whole feature map where pixels in overlapping regions are summed up. These two
modules are first used in tokenization before Transformer layers and
de-tokenization after Transformer layers, for effective mapping between tokens
and features. Therefore, sub-patch level information interaction is enabled for
more effective feature propagation between neighboring patches, resulting in
synthesizing vivid content for hole regions in videos. Moreover, in FuseFormer,
we elaborately insert the soft composition and soft split into the feed-forward
network, enabling the 1D linear layers to have the capability of modelling 2D
structure. And, the sub-patch level feature fusion ability is further enhanced.
In both quantitative and qualitative evaluations, our proposed FuseFormer
surpasses state-of-the-art methods. We also conduct detailed analysis to
examine its superiority.

FuseFormer 是一种针对视频修复任务的 Transformer 模型，通过使用细粒度特征融合的新型软分割和软组合操作，实现了更加有效的特征传播和内容合成，优于现有技术方法。