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

Abstract 3D human pose estimation and mesh recovery have attracted widespread research
interest in many areas, such as computer vision, autonomous driving, and robotics. Deep …

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 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 …

Graph convolutional network (GCN) has achieved great success in single hand
reconstruction task, while interacting two-hand reconstruction by GCN remains unexplored …

We propose a robust and accurate method for estimating the 3D poses of two hands in close
interaction from a single color image. This is a very challenging problem, as large occlusions …

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 …

In this work, we propose a framework for single-view hand mesh reconstruction, which can
simultaneously achieve high reconstruction accuracy, fast inference speed, and temporal …

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