  The objective of this work is to deblur face videos. We propose a method that
tackles this problem from two directions: (1) enhancing the blurry frames, and
(2) treating the blurry frames as missing values and estimate them by
interpolation. These approaches are complementary to each other, and their
combination outperforms individual ones. We also introduce a novel module that
leverages the structure of faces for finding positional offsets between video
frames. This module can be integrated into the processing pipelines of both
approaches, improving the quality of the final outcome. Experiments on three
real and synthetically generated blurry video datasets show that our method
outperforms the previous state-of-the-art methods by a large margin in terms of
both quantitative and qualitative results.
