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 …

Human activity recognition is essential in many domains, including the medical and smart
home sectors. Using deep learning, we conduct a comprehensive survey of current state …

To model the indeterminacy of human behaviors, stochastic trajectory prediction requires a
sophisticated multi-modal distribution of future trajectories. Emerging diffusion models have …

Learning to predict agent motions with relationship reasoning is important for many
applications. In motion prediction tasks, maintaining motion equivariance under Euclidean …

This paper tackles the problem of human motion prediction, consisting in forecasting future
body poses from historically observed sequences. State-of-the-art approaches provide good …

Many skeletal action recognition models use GCNs to represent the human body by 3D
body joints connected body parts. GCNs aggregate one-or few-hop graph neighbourhoods …

In recent years, Human Activity Recognition (HAR) has become one of the most important
research topics in the domains of health and human-machine interaction. Many Artificial …

This paper presents a high-quality human motion prediction method that accurately predicts
future human poses given observed ones. Our method is based on the observation that a …

We propose novel dynamic multiscale graph neural networks (DMGNN) to predict 3D
skeleton-based human motions. The core idea of DMGNN is to use a multiscale graph to …

Graph convolutional networks have been widely used for skeleton-based action recognition
due to their excellent modeling ability of non-Euclidean data. As the graph convolution is a …