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

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 …

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 …

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

Stochastic human motion prediction (HMP) has generally been tackled with generative
adversarial networks and variational autoencoders. Most prior works aim at predicting highly …

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

Pedestrian trajectory prediction is critical in many vision tasks but challenging due to the
multimodality of the future trajectory. Most existing methods predict multimodal trajectories …

In this work, we present MotionMixer, an efficient 3D human body pose forecasting model
based solely on multi-layer perceptrons (MLPs). MotionMixer learns the spatial-temporal 3D …

Predicting diverse human motions given a sequence of historical poses has received
increasing attention. Despite rapid progress, existing work captures the multi-modal nature …