  Human motion prediction is essential for tasks such as human motion analysis
and human-robot interactions. Most existing approaches have been proposed to
realize motion prediction. However, they ignore an important task, the
evaluation of the quality of the predicted result. It is far more enough for
current approaches in actual scenarios because people can't know how to
interact with the machine without the evaluation of prediction, and unreliable
predictions may mislead the machine to harm the human. Hence, we propose an
uncertainty-aware framework for human motion prediction (UA-HMP). Concretely,
we first design an uncertainty-aware predictor through Gaussian modeling to
achieve the value and the uncertainty of predicted motion. Then, an
uncertainty-guided learning scheme is proposed to quantitate the uncertainty
and reduce the negative effect of the noisy samples during optimization for
better performance. Our proposed framework is easily combined with current SOTA
baselines to overcome their weakness in uncertainty modeling with slight
parameters increment. Extensive experiments also show that they can achieve
better performance in both short and long-term predictions in H3.6M, CMU-Mocap.
