Human pose estimation aims to locate the human body parts and build human body
representation (eg, body skeleton) from input data such as images and videos. It has drawn …

Abstract 3D human pose data collected in controlled laboratory settings present challenges
for pose estimators that generalize across diverse scenarios. To address this domain …

Supervised human action recognition methods based on skeleton data have achieved
impressive performance recently. However, many current works emphasize the design of …

Despite recent advancements in masked skeleton modeling and visual-language pre-
training, no method has yet been proposed to explore capturing and utilizing the rich …

Contrastive learning for self-supervised skeleton-based action recognition has recently
received attention. It has been observed that local crops, containing partial action …

Contrastive learning has achieved great success in skeleton-based action recognition.
However, most existing approaches encode the skeleton sequences as entangled …

Graph Convolutional Networks (GCNs) have been widely used in skeleton-based human
action recognition and have achieved promising results. However, current GCN-based …

Human pose analysis has garnered significant attention within both the research community
and practical applications, owing to its expanding array of uses, including gaming, video …

Contrastive learning has been widely applied to self-supervised skeleton-based single-
person action recognition. However, directly employing single-person contrastive learning …

Recently, skeleton-based human action recognition has received widespread attention in
computer vision community. However, most existing research focuses on improving the …