  Video deblurring is still an unsolved problem due to the challenging
spatio-temporal modeling process. While existing convolutional neural
network-based methods show a limited capacity for effective spatial and
temporal modeling for video deblurring. This paper presents VDTR, an effective
Transformer-based model that makes the first attempt to adapt Transformer for
video deblurring. VDTR exploits the superior long-range and relation modeling
capabilities of Transformer for both spatial and temporal modeling. However, it
is challenging to design an appropriate Transformer-based model for video
deblurring due to the complicated non-uniform blurs, misalignment across
multiple frames and the high computational costs for high-resolution spatial
modeling. To address these problems, VDTR advocates performing attention within
non-overlapping windows and exploiting the hierarchical structure for
long-range dependencies modeling. For frame-level spatial modeling, we propose
an encoder-decoder Transformer that utilizes multi-scale features for
deblurring. For multi-frame temporal modeling, we adapt Transformer to fuse
multiple spatial features efficiently. Compared with CNN-based methods, the
proposed method achieves highly competitive results on both synthetic and
real-world video deblurring benchmarks, including DVD, GOPRO, REDS and BSD. We
hope such a Transformer-based architecture can serve as a powerful alternative
baseline for video deblurring and other video restoration tasks. The source
code will be available at \url{https://github.com/ljzycmd/VDTR}.
