  Data generation is a data augmentation technique for enhancing the
generalization ability for skeleton-based human action recognition. Most
existing data generation methods face challenges to ensure the temporal
consistency of the dynamic information for action. In addition, the data
generated by these methods lack diversity when only a few training samples are
available. To solve those problems, We propose a novel active generative
network (AGN), which can adaptively learn various action categories by motion
style transfer to generate new actions when the data for a particular action is
only a single sample or few samples. The AGN consists of an action generation
network and an uncertainty metric network. The former, with ST-GCN as the
Backbone, can implicitly learn the morphological features of the target action
while preserving the category features of the source action. The latter guides
generating actions. Specifically, an action recognition model generates
prediction vectors for each action, which is then scored using an uncertainty
metric. Finally, UMN provides the uncertainty sampling basis for the generated
actions.
