Human motion generation aims to generate natural human pose sequences and shows
immense potential for real-world applications. Substantial progress has been made recently …

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 …

We present an approach to reconstruct humans and track them over time. At the core of our
approach, we propose a fully" transformerized" version of a network for human mesh …

We show, for the first time, that neural networks trained only on synthetic data achieve state-
of-the-art accuracy on the problem of 3D human pose and shape (HPS) estimation from real …

The combination of deep learning, artist-curated scans, and Implicit Functions (IF), is
enabling the creation of detailed, clothed, 3D humans from images. However, existing …

We present GaussianAvatar an efficient approach to creating realistic human avatars with
dynamic 3D appearances from a single video. We start by introducing animatable 3D …

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 …

Whole-body mesh recovery aims to estimate the 3D human body, face, and hands
parameters from a single image. It is challenging to perform this task with a single network …

The estimation of 3D human motion from video has progressed rapidly but current methods
still have several key limitations. First most methods estimate the human in camera …

Expressive human pose and shape estimation (EHPS) unifies body, hands, and face motion
capture with numerous applications. Despite encouraging progress, current state-of-the-art …