Three-dimensional skeleton-based action recognition (3D SAR) has gained important attention within the computer vision community, owing to the inherent advantages offered by skeleton data. As a result, a plethora of impressive works, including those based on conventional handcrafted features and learned feature extraction methods, have been conducted over the years. However, prior surveys on action recognition have primarily focused on video or red-green-blue (RGB) data-dominated approaches, with limited coverage of reviews related to skeleton data. Furthermore, despite the extensive application of deep learning methods in this field, there has been a notable absence of research that provides an introductory or comprehensive review from the perspective of deep learning architectures. To address these limitations, this survey first underscores the importance of action recognition and emphasizes the significance of 3-dimensional (3D) skeleton data as a valuable modality. Subsequently, we provide a comprehensive introduction to mainstream action recognition techniques based on 4 fundamental deep architectures, i.e., recurrent neural networks, convolutional neural networks, graph convolutional network, and Transformers. All methods with the corresponding architectures are then presented in a data-driven manner with detailed discussion. Finally, we offer insights into the current largest 3D skeleton dataset, NTU-RGB+D, and its new edition, NTU-RGB+D 120, along with an overview of several top-performing algorithms on these datasets. To the best of our knowledge, this research represents the first comprehensive discussion of deep learning-based action recognition using 3D skeleton data.

Extensive research has explored human motion generation, but the generated sequences are influenced by different motion styles. For instance, the act of walking with joy and sorrow evokes distinct effects on a character’s motion. Due to the difficulties in motion capture with styles, the available data for style research are also limited. To address the problems, we propose ASMNet, an action and style-conditioned motion generative network. This network ensures that the generated human motion sequences not only comply with the provided action label but also exhibit distinctive stylistic features. To extract motion features from human motion sequences, we design a spatial temporal extractor. Moreover, we use the adaptive instance normalization layer to inject style into the target motion. Our results are comparable to state-of-the-art approaches and display a substantial advantage in both quantitative and qualitative evaluations. The code is available at https://github.com/ZongYingLi/ASMNet.git.