  Human motion prediction aims to predict future 3D skeletal sequences by
giving a limited human motion as inputs. Two popular methods, recurrent neural
networks and feed-forward deep networks, are able to predict rough motion
trend, but motion details such as limb movement may be lost. To predict more
accurate future human motion, we propose an Adversarial Refinement Network
(ARNet) following a simple yet effective coarse-to-fine mechanism with novel
adversarial error augmentation. Specifically, we take both the historical
motion sequences and coarse prediction as input of our cascaded refinement
network to predict refined human motion and strengthen the refinement network
with adversarial error augmentation. During training, we deliberately introduce
the error distribution by learning through the adversarial mechanism among
different subjects. In testing, our cascaded refinement network alleviates the
prediction error from the coarse predictor resulting in a finer prediction
robustly. This adversarial error augmentation provides rich error cases as
input to our refinement network, leading to better generalization performance
on the testing dataset. We conduct extensive experiments on three standard
benchmark datasets and show that our proposed ARNet outperforms other
state-of-the-art methods, especially on challenging aperiodic actions in both
short-term and long-term predictions.
