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 address the problem of regressing 3D human pose and shape from a single image with
a focus on 3D accuracy. The current best methods leverage large datasets of 3D pseudo …

We present DiffHuman a probabilistic method for photorealistic 3D human reconstruction
from a single RGB image. Despite the ill-posed nature of this problem most methods are …

The regression of 3D Human Pose and Shape (HPS) from an image is becoming
increasingly accurate. This makes the results useful for downstream tasks like human action …

Normalizing flows have proven their efficacy for density estimation in Euclidean space but
their application to rotational representations crucial in various domains such as robotics or …

Monocular 3D human mesh estimation is an ill-posed problem characterized by inherent
ambiguity and occlusion. While recent probabilistic methods propose generating multiple …

While current methods have shown promising progress on estimating 3D human motion
from monocular videos their motion estimates are often physically unrealistic because they …

Human Pose and Shape Estimation (HPSE) from RGB images can be broadly categorized
into two main groups: parametric and non-parametric approaches. Parametric techniques …

Despite its extensive range of potential applications in virtual reality and augmented reality,
3D hand pose estimation from RGB image remains a very challenging problem. The …

Leveraging wearable devices for motion reconstruction has emerged as an economical and
viable technique. Certain methodologies employ sparse Inertial Measurement Units (IMUs) …