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

BMBC:Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation