  In spite of the great progress in human motion prediction, it is still a
challenging task to predict those aperiodic and complicated motions. We believe
that to capture the correlations among human body components is the key to
understand the human motion. In this paper, we propose a novel multiscale graph
convolution network (MGCN) to address this problem. Firstly, we design an
adaptive multiscale interactional encoding module (MIEM) which is composed of
two sub modules: scale transformation module and scale interaction module to
learn the human body correlations. Secondly, we apply a coarse-to-fine decoding
strategy to decode the motions sequentially. We evaluate our approach on two
standard benchmark datasets for human motion prediction: Human3.6M and CMU
motion capture dataset. The experiments show that the proposed approach
achieves the state-of-the-art performance for both short-term and long-term
prediction especially in those complicated action category.
