Motion Inbetweening via Deep Δ-Interpolator
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We show that the task of synthesizing missing middle frames, commonly known as motion inbetweening 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 Δ-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 inbetweening in the reference frame local to input frames is more accurate and robust than inbetweening in the global (world) reference frame advocated in previous work. Our code is publicly available at https://github.com/boreshkinai/delta-interpolator.
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