Predicting human motion is critical for assistive robots and AR/VR applications, where the
interaction with humans needs to be safe and comfortable. Meanwhile, an accurate …

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

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 …

AI-generated content (AIGC) methods aim to produce text, images, videos, 3D assets, and
other media using AI algorithms. Due to its wide range of applications and the demonstrated …

The success of the transformer architecture in natural language processing has recently
triggered attention in the computer vision field. The transformer has been used as a …

Predicting 3D human poses in real-world scenarios, also known as human pose forecasting,
is inevitably subject to noisy inputs arising from inaccurate 3D pose estimations and …

In recent years, the rapid advancement of deep learning and the advent of extensive human
motion datasets have significantly enhanced the prominence of human motion prediction …

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

In order to build artificial intelligence systems that can perceive and reason with human
behavior in the real world, we must first design models that conduct complex spatio-temporal …

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