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

Abstract 3D reconstruction of deformable (or non‐rigid) scenes from a set of monocular 2D
image observations is a long‐standing and actively researched area of computer vision and …

3D avatar creation plays a crucial role in the digital age. However, the whole production
process is prohibitively time-consuming and labor-intensive. To democratize this technology …

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 …

Generating high-quality and diverse human images is an important yet challenging task in
vision and graphics. However, existing generative models often fall short under the high …

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 …

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 …

Abstract Existing Human NeRF methods for reconstructing 3D humans typically rely on
multiple 2D images from multi-view cameras or monocular videos captured from fixed …

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 …

Realistic human-centric rendering plays a key role in both computer vision and computer
graphics. Rapid progress has been made in the algorithm aspect over the years, yet existing …