Abstract. Human motion prediction, forecasting human motion in a
few milliseconds conditioning on a historical 3D skeleton sequence, is a
long-standing problem in computer vision and robotic vision. Existing
forecasting algorithms rely on extensive annotated motion capture data
and are brittle to novel actions. This paper addresses the problem of
few-shot human motion prediction, in the spirit of the recent progress
on few-shot learning and meta-learning. More precisely, our approach is
based on the insight that having a good generalization from few examples
relies on both a generic initial model and an effective strategy for adapting this model to novel tasks. To accomplish this, we propose proactive
and adaptive meta-learning (PAML) that introduces a novel combination of model-agnostic meta-learning and model regression networks and
unifies them into an integrated, end-to-end framework. By doing so, our
meta-learner produces a generic initial model through aggregating contextual information from a variety of prediction tasks, while effectively
adapting this model for use as a task-specific one by leveraging learningto-learn knowledge about how to transform few-shot model parameters
to many-shot model parameters. The resulting PAML predictor model
significantly improves the prediction performance on the heavily benchmarked H3.6M dataset in the small-sample size regime