  Various blur distortions in video will cause negative impact on both human
viewing and video-based applications, which makes motion-robust deblurring
methods urgently needed. Most existing works have strong dataset dependency and
limited generalization ability in handling challenging scenarios, like blur in
low contrast or severe motion areas, and non-uniform blur. Therefore, we
propose a PRiOr-enlightened and MOTION-robust video deblurring model
(PROMOTION) suitable for challenging blurs. On the one hand, we use 3D group
convolution to efficiently encode heterogeneous prior information, explicitly
enhancing the scenes' perception while mitigating the output's artifacts. On
the other hand, we design the priors representing blur distribution, to better
handle non-uniform blur in spatio-temporal domain. Besides the classical camera
shake caused global blurry, we also prove the generalization for the downstream
task suffering from local blur. Extensive experiments demonstrate we can
achieve the state-of-the-art performance on well-known REDS and GoPro datasets,
and bring machine task gain.
