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.

翻译：数据生成是一种数据增强技术，旨在提升基于骨架的人体动作识别的泛化能力。现有的大多数数据生成方法在确保动作动态信息的时间一致性方面面临挑战。此外，当仅有少量训练样本可用时，这些方法生成的数据缺乏多样性。为解决这些问题，我们提出了一种新颖的主动生成网络（AGN），该网络能够通过运动风格迁移自适应地学习多种动作类别，在特定动作数据仅为单样本或少量样本时生成新动作。AGN由动作生成网络和不确定性度量网络组成。前者以ST-GCN为骨干网络，能在保留源动作类别特征的同时隐式学习目标动作的形态特征；后者则指导动作生成。具体而言，动作识别模型为每个动作生成预测向量，并通过不确定性度量进行评分。最终，不确定性度量网络（UMN）为生成动作提供基于不确定性采样的依据。