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 …

Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (\emph {eg,} social …

We present PyMAF-X, a regression-based approach to recovering a parametric full-body
model from a single image. This task is very challenging since minor parametric deviation …

Humans intuitively understand that inanimate objects do not move by themselves, but that
state changes are typically caused by human manipulation (eg, the opening of a book). This …

We present an approach that can reconstruct hands in 3D from monocular input. Our
approach for Hand Mesh Recovery HaMeR follows a fully transformer-based architecture …

Signed distance functions (SDFs) is an attractive framework that has recently shown
promising results for 3D shape reconstruction from images. SDFs seamlessly generalize to …

Humans commonly work with multiple objects in daily life and can intuitively transfer
manipulation skills to novel objects by understanding object functional regularities. However …

Despite recent achievements, existing 3D interacting hands recovery methods have shown
results mainly on motion capture (MoCap) environments, not on in-the-wild (ITW) ones. This …

Reconstructing two hands from monocular RGB images is challenging due to frequent
occlusion and mutual confusion. Existing methods mainly learn an entangled representation …

The two-hand interaction is one of the most challenging signals to analyze due to the self-
similarity, complicated articulations, and occlusions of hands. Although several datasets …