Human pose estimation aims to locate the human body parts and build human body
representation (eg, body skeleton) from input data such as images and videos. It has drawn …

Abstract Three-dimensional (3D) human pose estimation involves estimating the articulated
3D joint locations of a human body from an image or video. Due to its widespread …

Estimating 3D human poses from monocular videos is a challenging task due to depth
ambiguity and self-occlusion. Most existing works attempt to solve both issues by exploiting …

Recent transformer-based solutions have been introduced to estimate 3D human pose from
2D keypoint sequence by considering body joints among all frames globally to learn spatio …

Monocular 3D human pose estimation is quite challenging due to the inherent ambiguity
and occlusion, which often lead to high uncertainty and indeterminacy. On the other hand …

Regression-based methods have recently shown promising results in reconstructing human
meshes from monocular images. By directly map** raw pixels to model parameters, these …

Unconditional human image generation is an important task in vision and graphics, enabling
various applications in the creative industry. Existing studies in this field mainly focus on …

Abstract Model-based 3D pose and shape estimation methods reconstruct a full 3D mesh for
the human body by estimating several parameters. However, learning the abstract …

Most of the previous image-based 3D human pose and mesh estimation methods estimate
parameters of the human mesh model from an input image. However, directly regressing the …

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 …