Dynamic Compositional Graph Convolutional Network for Efficient
  Composite Human Motion Prediction
Wanying Zhang Shen Zhao Fanyang Meng Songtao Wu Mengyuan Liu
With potential applications in fields including intelligent surveillance and
human-robot interaction, the human motion prediction task has become a hot
research topic and also has achieved high success, especially using the recent
Graph Convolutional Network (GCN). Current human motion prediction task usually
focuses on predicting human motions for atomic actions. Observing that atomic
actions can happen at the same time and thus formulating the composite actions,
we propose the composite human motion prediction task. To handle this task, we
first present a Composite Action Generation (CAG) module to generate synthetic
composite actions for training, thus avoiding the laborious work of collecting
composite action samples. Moreover, we alleviate the effect of composite
actions on demand for a more complicated model by presenting a Dynamic
Compositional Graph Convolutional Network (DC-GCN). Extensive experiments on
the Human3.6M dataset and our newly collected CHAMP dataset consistently verify
the efficiency of our DC-GCN method, which achieves state-of-the-art motion
prediction accuracies and meanwhile needs few extra computational costs than
traditional GCN-based human motion methods.