  Existing works address the problem of generating high frame-rate sharp videos
by separately learning the frame deblurring and frame interpolation modules.
Most of these approaches have a strong prior assumption that all the input
frames are blurry whereas in a real-world setting, the quality of frames
varies. Moreover, such approaches are trained to perform either of the two
tasks - deblurring or interpolation - in isolation, while many practical
situations call for both. Different from these works, we address a more
realistic problem of high frame-rate sharp video synthesis with no prior
assumption that input is always blurry. We introduce a novel architecture,
Adaptive Latent Attention Network (ALANET), which synthesizes sharp high
frame-rate videos with no prior knowledge of input frames being blurry or not,
thereby performing the task of both deblurring and interpolation. We
hypothesize that information from the latent representation of the consecutive
frames can be utilized to generate optimized representations for both frame
deblurring and frame interpolation. Specifically, we employ combination of
self-attention and cross-attention module between consecutive frames in the
latent space to generate optimized representation for each frame. The optimized
representation learnt using these attention modules help the model to generate
and interpolate sharp frames. Extensive experiments on standard datasets
demonstrate that our method performs favorably against various state-of-the-art
approaches, even though we tackle a much more difficult problem.
