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 …

The reconstruction of 3D object from a single image is an important task in the field of
computer vision. In recent years, 3D reconstruction of single image using deep learning …

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 …

To understand how people look, interact, or perform tasks, we need to quickly and
accurately capture their 3D body, face, and hands together from an RGB image. Most …

We introduce a simple and effective network architecture for monocular 3D hand pose
estimation consisting of an image encoder followed by a mesh convolutional decoder that is …

We study the problem of understanding hand-object interactions from 2D images in the wild.
This requires reconstructing both the hand and the object in 3D, which is challenging …

Recovering whole-body mesh by inferring the abstract pose and shape parameters from
visual content can obtain 3D bodies with realistic structures. However, the inferring process …

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

Egocentric videos offer fine-grained information for high-fidelity modeling of human
behaviors. Hands and interacting objects are one crucial aspect of understanding a viewer's …

Recent work achieved impressive progress towards joint reconstruction of hands and
manipulated objects from monocular color images. Existing methods focus on two …