  We propose novel dynamic multiscale graph neural networks (DMGNN) to predict
3D skeleton-based human motions. The core idea of DMGNN is to use a multiscale
graph to comprehensively model the internal relations of a human body for
motion feature learning. This multiscale graph is adaptive during training and
dynamic across network layers. Based on this graph, we propose a multiscale
graph computational unit (MGCU) to extract features at individual scales and
fuse features across scales. The entire model is action-category-agnostic and
follows an encoder-decoder framework. The encoder consists of a sequence of
MGCUs to learn motion features. The decoder uses a proposed graph-based gate
recurrent unit to generate future poses. Extensive experiments show that the
proposed DMGNN outperforms state-of-the-art methods in both short and long-term
predictions on the datasets of Human 3.6M and CMU Mocap. We further investigate
the learned multiscale graphs for the interpretability. The codes could be
downloaded from https://github.com/limaosen0/DMGNN.
