Human Action Recognition (HAR) aims to understand human behavior and assign a label to
each action. It has a wide range of applications, and therefore has been attracting increasing …

Three-dimensional skeleton-based action recognition (3D SAR) has gained important
attention within the computer vision community, owing to the inherent advantages offered by …

One essential problem in skeleton-based action recognition is how to extract discriminative
features over all skeleton joints. However, the complexity of the recent State-Of-The-Art …

Research on depth-based human activity analysis achieved outstanding performance and
demonstrated the effectiveness of 3D representation for action recognition. The existing …

In skeleton-based action recognition, graph convolutional networks (GCNs) have achieved
remarkable success. Nevertheless, how to efficiently model the spatial-temporal skeleton …

Point cloud videos exhibit irregularities and lack of order along the spatial dimension where
points emerge inconsistently across different frames. To capture the dynamics in point cloud …

raph Convolutional Networks (GCNs) have attracted increasing interests for the task of
skeleton-based action recognition. The key lies in the design of the graph structure, which …

Human action recognition (HAR) in RGB-D videos has been widely investigated since the
release of affordable depth sensors. Currently, unimodal approaches (eg, skeleton-based …

Current methods for skeleton-based human action recognition usually work with complete
skeletons. However, in real scenarios, it is inevitable to capture incomplete or noisy …

Despite the great progress in human motion prediction, it remains a challenging task due to
the complicated structural dynamics of human behaviors. In this paper, we address this …