Abstract 3D human motion prediction, predicting future poses from a given sequence, is an
issue of great significance and challenge in computer vision and machine intelligence …

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

Diverse human motion prediction aims at predicting multiple possible future pose
sequences from a sequence of observed poses. Previous approaches usually employ deep …

The mainstream motion prediction methods usually focus on short-term prediction, and their
predicted long-term motions often fall into an average pose, ie, the freezing forecasting …

Long-term and accurate forecasting is the long-standing pursuit of the human motion
prediction task. Existing methods typically suffer from dramatic degradation in prediction …

Human motion prediction is challenging due to the complex spatiotemporal feature
modeling. Among all methods, graph convolution networks (GCNs) are extensively utilized …

Predicting the future trajectories of multiple agents is essential for various applications in
real life, such as surveillance systems, autonomous driving and social robots. The trajectory …

Pose and motion priors are crucial for recovering realistic and accurate human motion from
noisy observations. Substantial progress has been made on pose and shape estimation …

To achieve seamless collaboration between robots and humans in a shared environment,
accurately predicting future human movements is essential. Human motion prediction has …

Human pose prediction, interchangeably known as human pose forecasting, is a daunting
endeavor within computer vision. Owing to its pivotal role in many advanced applications …