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Video Frame Interpolation (VFI) aims to synthesize non-existent intermediateframes between existent frames. Flow-based VFI algorithms estimate intermediatemotion fields to warp the existent frames. Real-world motions' complexity andthe reference frame's absence make motion estimation challenging. Manystate-of-the-art approaches explicitly model the correlations between twoneighboring frames for more accurate motion estimation. In common approaches,the receptive field of correlation modeling at higher resolution depends on themotion fields estimated beforehand. Such receptive field dependency makescommon motion estimation approaches poor at coping with small and fast-movingobjects. To better model correlations and to produce more accurate motionfields, we propose the Densely Queried Bilateral Correlation (DQBC) that getsrid of the receptive field dependency problem and thus is more friendly tosmall and fast-moving objects. The motion fields generated with the help ofDQBC are further refined and up-sampled with context features. After the motionfields are fixed, a CNN-based SynthNet synthesizes the final interpolatedframe. Experiments show that our approach enjoys higher accuracy and lessinference time than the state-of-the-art. Source code is available athttps://github.com/kinoud/DQBC.

Video Frame Interpolation with Densely Queried Bilateral Correlation