  This work aims to provide a deep-learning solution for the motion
interpolation task. Previous studies solve it with geometric weight functions.
Some other works propose neural networks for different problem settings with
consecutive pose sequences as input. However, motion interpolation is a more
complex problem that takes isolated poses (e.g., only one start pose and one
end pose) as input. When applied to motion interpolation, these deep learning
methods have limited performance since they do not leverage the flexible
dependencies between interpolation frames as the original geometric formulas
do. To realize this interpolation characteristic, we propose a novel framework,
referred to as \emph{Shuffled AutoRegression}, which expands the autoregression
to generate in arbitrary (shuffled) order and models any inter-frame
dependencies as a directed acyclic graph. We further propose an approach to
constructing a particular kind of dependency graph, with three stages assembled
into an end-to-end spatial-temporal motion Transformer. Experimental results on
one of the current largest datasets show that our model generates vivid and
coherent motions from only one start frame to one end frame and outperforms
competing methods by a large margin. The proposed model is also extensible to
multiple keyframes' motion interpolation tasks and other areas' interpolation.
