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 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 realistic 3D worlds occupied by moving humans has many applications in
games, architecture, and synthetic data creation. But generating such scenes is expensive …

As it is hard to calibrate single-view RGB images in the wild, existing 3D human mesh
reconstruction (3DHMR) methods either use a constant large focal length or estimate one …

Abstract We introduce Structured 3D Features, a model based on a novel implicit 3D
representation that pools pixel-aligned image features onto dense 3D points sampled from a …

Hand, the bearer of human productivity and intelligence, is receiving much attention due to
the recent fever of digital twins. Among different hand morphable models, MANO has been …

Synthetic data has emerged as a promising source for 3D human research as it offers low-
cost access to large-scale human datasets. To advance the diversity and annotation quality …

Segmenting humans in 3D indoor scenes has become increasingly important with the rise of
human-centered robotics and AR/VR applications. To this end, we propose the task of joint …

Learning-based approaches to monocular motion capture have recently shown promising
results by learning to regress in a data-driven manner. However due to the challenges in …

Learning-based approaches to monocular motion capture have recently shown promising
results by learning to regress in a data-driven manner. However, due to the challenges in …