Estimating human pose and shape from monocular images is a long-standing problem in
computer vision. Since the release of statistical body models, 3D human mesh recovery has …

Human motion modeling is important for many modern graphics applications, which typically
require professional skills. In order to remove the skill barriers for laymen, recent motion …

In this work, we investigate a simple and must-known conditional generative framework
based on Vector Quantised-Variational AutoEncoder (VQ-VAE) and Generative Pre-trained …

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 …

Denoising diffusion models have shown great promise in human motion synthesis
conditioned on natural language descriptions. However, integrating spatial constraints, such …

Given a series of natural language descriptions, our task is to generate 3D human motions
that correspond semantically to the text, and follow the temporal order of the instructions. In …

This paper presents a novel approach to generating the 3D motion of a human interacting
with a target object, with a focus on solving the challenge of synthesizing long-range and …

We present an approach for 3D global human mesh recovery from monocular videos
recorded with dynamic cameras. Our approach is robust to severe and long-term occlusions …

Recovering high-quality 3D human motion in complex scenes from monocular videos is
important for many applications, ranging from AR/VR to robotics. However, capturing …

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 …