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 …

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

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

In the realm of stochastic human motion prediction (SHMP), researchers have often turned to
generative models like GANS, VAEs and diffusion models. However, most previous …

Human motion prediction is crucial for facilitating human–robot collaboration in customized
assembly tasks. However, existing research primarily focuses on predicting limited human …

Predicting human motion plays a crucial role in ensuring a safe and effective human-robot
close collaboration in intelligent remanufacturing systems of the future. Existing works can …

Deriving sophisticated 3D motions from sparse keyframes is a particularly challenging
problem, due to continuity and exceptionally skeletal precision. The action features are often …

We focus on the human-humanoid interaction task optionally with an object. We propose a
new task named online full-body motion reaction synthesis, which generates humanoid …

Studies on the automatic processing of 3D human pose data have flourished in the recent
past. In this paper, we are interested in the generation of plausible and diverse future human …

Abstract Stochastic Human Motion Prediction (HMP) aims to predict multiple possible future
human pose sequences from observed ones. Most prior works learn motion distributions …