Motion Inbetweening via Deep $Δ$-Interpolator

We show that the task of synthesizing missing middle frames, commonly known as motion in-betweening in the animation industry, can be solved more accurately and effectively if a deep learning interpolator operates in the delta mode, using the spherical linear interpolator as a baseline. We demonstrate our empirical findings on the publicly available LaFAN1 dataset. We further generalize this result by showing that the $\Delta$-regime is viable with respect to the reference of the last known frame (also known as the zero-velocity model). This supports the more general conclusion that deep in-betweening in the reference frame local to input frames is more accurate and robust than in-betweening in the global (world) reference frame advocated in previous work. Our code is publicly available at https://github.com/boreshkinai/delta-interpolator.