  Combining skeleton structure with graph convolutional networks has achieved
remarkable performance in human action recognition. Since current research
focuses on designing basic graph for representing skeleton data, these
embedding features contain basic topological information, which cannot learn
more systematic perspectives from skeleton data. In this paper, we overcome
this limitation by proposing a novel framework, which unifies 15 graph
embedding features into the graph convolutional network for human action
recognition, aiming to best take advantage of graph information to distinguish
key joints, bones, and body parts in human action, instead of being exclusive
to a single feature or domain. Additionally, we fully investigate how to find
the best graph features of skeleton structure for improving human action
recognition. Besides, the topological information of the skeleton sequence is
explored to further enhance the performance in a multi-stream framework.
Moreover, the unified graph features are extracted by the adaptive methods on
the training process, which further yields improvements. Our model is validated
by three large-scale datasets, namely NTU-RGB+D, Kinetics and SYSU-3D, and
outperforms the state-of-the-art methods. Overall, our work unified graph
embedding features to promotes systematic research on human action recognition.
