  Recently, skeleton-based human action has become a hot research topic because
the compact representation of human skeletons brings new blood to this research
domain. As a result, researchers began to notice the importance of using RGB or
other sensors to analyze human action by extracting skeleton information.
Leveraging the rapid development of deep learning (DL), a significant number of
skeleton-based human action approaches have been presented with fine-designed
DL structures recently. However, a well-trained DL model always demands
high-quality and sufficient data, which is hard to obtain without costing high
expenses and human labor. In this paper, we introduce a novel data augmentation
method for skeleton-based action recognition tasks, which can effectively
generate high-quality and diverse sequential actions. In order to obtain
natural and realistic action sequences, we propose denoising diffusion
probabilistic models (DDPMs) that can generate a series of synthetic action
sequences, and their generation process is precisely guided by a
spatial-temporal transformer (ST-Trans). Experimental results show that our
method outperforms the state-of-the-art (SOTA) motion generation approaches on
different naturality and diversity metrics. It proves that its high-quality
synthetic data can also be effectively deployed to existing action recognition
models with significant performance improvement.
