  Video interpolation increases the temporal resolution of a video sequence by
synthesizing intermediate frames between two consecutive frames. We propose a
novel deep-learning-based video interpolation algorithm based on bilateral
motion estimation. First, we develop the bilateral motion network with the
bilateral cost volume to estimate bilateral motions accurately. Then, we
approximate bi-directional motions to predict a different kind of bilateral
motions. We then warp the two input frames using the estimated bilateral
motions. Next, we develop the dynamic filter generation network to yield
dynamic blending filters. Finally, we combine the warped frames using the
dynamic blending filters to generate intermediate frames. Experimental results
show that the proposed algorithm outperforms the state-of-the-art video
interpolation algorithms on several benchmark datasets.
