  Human motion prediction is an important and challenging task in many computer
vision application domains. Recent work concentrates on utilizing the timing
processing ability of recurrent neural networks (RNNs) to achieve smooth and
reliable results in short-term prediction. However, as evidenced by previous
work, RNNs suffer from errors accumulation, leading to unreliable results. In
this paper, we propose a simple feed-forward deep neural network for motion
prediction, which takes into account temporal smoothness and spatial
dependencies between human body joints. We design a Multi-scale Spatio-temporal
graph convolutional networks (GCNs) to implicitly establish the Spatio-temporal
dependence in the process of human movement, where different scales fused
dynamically during training. The entire model is suitable for all actions and
follows a framework of encoder-decoder. The encoder consists of temporal GCNs
to capture motion features between frames and semi-autonomous learned spatial
GCNs to extract spatial structure among joint trajectories. The decoder uses
temporal convolution networks (TCNs) to maintain its extensive ability.
Extensive experiments show that our approach outperforms SOTA methods on the
datasets of Human3.6M and CMU Mocap while only requiring much lesser
parameters. Code will be available at https://github.com/yzg9353/DMSGCN.
