  We show that the task of synthesizing human motion conditioned on a set of
key frames can be solved more accurately and effectively if a deep learning
based interpolator operates in the delta mode using the spherical linear
interpolator as a baseline. We empirically demonstrate the strength of our
approach on publicly available datasets achieving state-of-the-art performance.
We further generalize these results 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 operating
in the reference frame local to input frames is more accurate and robust than
in the global (world) reference frame advocated in previous work. Our code is
publicly available at https://github.com/boreshkinai/delta-interpolator.
