Enhanced Bi-directional Motion Estimation for Video Frame Interpolation

We propose a simple yet effective algorithm for motion-based video frame interpolation. Existing motion-based interpolation methods typically rely on an off-the-shelf optical flow model or a U-Net based pyramid network for motion estimation, which either suffer from large model size or limited capacity in handling various challenging motion cases. In this work, we present a novel compact model to simultaneously estimate the bi-directional motions between input frames. It is designed by carefully adapting the ingredients (e.g., warping, correlation) in optical flow research for simultaneous bi-directional motion estimation within a flexible pyramid recurrent framework. Our motion estimator is extremely lightweight (15x smaller than PWC-Net), yet enables reliable handling of large and complex motion cases. Based on estimated bi-directional motions, we employ a synthesis network to fuse forward-warped representations and predict the intermediate frame. Our method achieves excellent performance on a broad range of frame interpolation benchmarks. Code and trained models are available at https://github.com/srcn-ivl/EBME.