Skeleton-based action recognition has rapidly become one of the most popular and
essential research topics in computer vision. The task is to analyze the characteristics of …

Remote photoplethysmography (rPPG) is an important technique for detecting human vital
signs and has received extensive attention. For a long time, researchers have focused …

Encouraged by the effectiveness of encoding temporal dynamics within the frequency
domain, recent human motion prediction systems prefer to first convert the motion …

3D Skeleton-based human action recognition has attracted increasing attention in recent
years. Most of the existing work focuses on supervised learning which requires a large …

3D human action recognition has received increasing attention due to its potential
application in video surveillance equipment. To guarantee satisfactory performance …

Currently, video behavior recognition is one of the most foundational tasks of computer
vision. The 2D neural networks of deep learning are built for recognizing pixel-level …

In the 3D skeleton-based action recognition task, learning rich spatial and temporal motion
patterns from body joints are two foundational yet under-explored problems. In this paper …

Graph convolution networks (GCNs) have been widely used and achieved fruitful progress
in the skeleton-based action recognition task. In GCNs, node interaction modeling …

Self-supervised representation learning has proven constructive for skeleton-based action
recognition. For better performance, existing methods mainly focus on 1) multi-modal data …

In this article, we propose a novel cascaded diffusion-based generative framework for text-
driven human motion synthesis, which exploits a strategy named GradUally Enriching …