Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …

Graph Neural Networks (GNNs) perform well in community detection and molecule
classification. Counterfactual Explanations (CE) provide counter-examples to overcome the …

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 …

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 …

Predicting future motion based on historical motion sequence is a fundamental problem in
computer vision, and it has wide applications in autonomous driving and robotics. Some …

Human motion prediction is a classical problem in computer vision and computer graphics,
which has a wide range of practical applications. Previous effects achieve great empirical …

Anomalies are rare and anomaly detection is often therefore framed as One-Class
Classification (OCC), ie trained solely on normalcy. Leading OCC techniques constrain the …

Exploring spatial-temporal dependencies from observed motions is one of the core
challenges of human motion prediction. Previous methods mainly focus on dedicated …

Graph convolutional network based methods that model the body-joints' relations, have
recently shown great promise in 3D skeleton-based human motion prediction. However …

Graph Convolutional Networks (GCN) which typically follows a neural message passing
framework to model dependencies among skeletal joints has achieved high success in …