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 …

In this paper, we present Motion-X, a large-scale 3D expressive whole-body motion dataset.
Existing motion datasets predominantly contain body-only poses, lacking facial expressions …

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

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 …

Human motion prediction is a challenging task due to the stochasticity and aperiodicity of
future poses. Recently, graph convolutional network has been proven to be very effective to …

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 …

Human pose forecasting is a complex structured-data sequence-modelling task, which has
received increasing attention, also due to numerous potential applications. Research has …

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 …

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

We propose a novel Transformer-based architecture for the task of generative modelling of
3D human motion. Previous work commonly relies on RNN-based models considering …