  Natural videos captured by consumer cameras often suffer from low framerate
and motion blur due to the combination of dynamic scene complexity, lens and
sensor imperfection, and less than ideal exposure setting. As a result,
computational methods that jointly perform video frame interpolation and
deblurring begin to emerge with the unrealistic assumption that the exposure
time is known and fixed. In this work, we aim ambitiously for a more realistic
and challenging task - joint video multi-frame interpolation and deblurring
under unknown exposure time. Toward this goal, we first adopt a variant of
supervised contrastive learning to construct an exposure-aware representation
from input blurred frames. We then train two U-Nets for intra-motion and
inter-motion analysis, respectively, adapting to the learned exposure
representation via gain tuning. We finally build our video reconstruction
network upon the exposure and motion representation by progressive
exposure-adaptive convolution and motion refinement. Extensive experiments on
both simulated and real-world datasets show that our optimized method achieves
notable performance gains over the state-of-the-art on the joint video x8
interpolation and deblurring task. Moreover, on the seemingly implausible x16
interpolation task, our method outperforms existing methods by more than 1.5 dB
in terms of PSNR.
