  The task of action-driven human motion prediction aims to forecast future
human motion based on the observed sequence while respecting the given action
label. It requires modeling not only the stochasticity within human motion but
the smooth yet realistic transition between multiple action labels. However,
the fact that most datasets do not contain such transition data complicates
this task. Existing work tackles this issue by learning a smoothness prior to
simply promote smooth transitions, yet doing so can result in unnatural
transitions especially when the history and predicted motions differ
significantly in orientations. In this paper, we argue that valid human motion
transitions should incorporate realistic leg movements to handle orientation
changes, and cast it as an action-conditioned in-betweening (ACB) learning task
to encourage transition naturalness. Because modeling all possible transitions
is virtually unreasonable, our ACB is only performed on very few selected
action classes with active gait motions, such as Walk or Run. Specifically, we
follow a two-stage forecasting strategy by first employing the motion diffusion
model to generate the target motion with a specified future action, and then
producing the in-betweening to smoothly connect the observation and prediction
to eventually address motion prediction. Our method is completely free from the
labeled motion transition data during training. To show the robustness of our
approach, we generalize our trained in-betweening learning model on one dataset
to two unseen large-scale motion datasets to produce natural transitions.
Extensive experimental evaluations on three benchmark datasets demonstrate that
our method yields the state-of-the-art performance in terms of visual quality,
prediction accuracy, and action faithfulness.
