  Video frame interpolation (VFI), which aims to synthesize intermediate frames
of a video, has made remarkable progress with development of deep convolutional
networks over past years. Existing methods built upon convolutional networks
generally face challenges of handling large motion due to the locality of
convolution operations. To overcome this limitation, we introduce a novel
framework, which takes advantage of Transformer to model long-range pixel
correlation among video frames. Further, our network is equipped with a novel
cross-scale window-based attention mechanism, where cross-scale windows
interact with each other. This design effectively enlarges the receptive field
and aggregates multi-scale information. Extensive quantitative and qualitative
experiments demonstrate that our method achieves new state-of-the-art results
on various benchmarks.
