This paper addresses the problem of 3D human pose and shape estimation from a single
image. Previous approaches consider a parametric model of the human body, SMPL, and …

We propose a scalable neural network framework to reconstruct the 3D mesh of a human
body from multi-view images, in the subspace of the SMPL model. Use of multi-view images …

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

Most of the recent deep learning-based 3D human pose and mesh estimation methods
regress the pose and shape parameters of human mesh models, such as SMPL and MANO …

Abstract Model-based human pose estimation is currently approached through two different
paradigms. Optimization-based methods fit a parametric body model to 2D observations in …

This work addresses the problem of model-based human pose estimation. Recent
approaches have made significant progress towards regressing the parameters of …

In this paper, we tackle the problem of 3D human shape estimation from single RGB images.
While the recent progress in convolutional neural networks has allowed impressive results …

We present a self-supervised human mesh recovery framework to infer human pose and
shape from monocular images in the absence of any paired supervision. Recent advances …

This work addresses the problem of estimating the full body 3D human pose and shape from
a single color image. This is a task where iterative optimization-based solutions have …

Compared to joint position, the accuracy of joint rotation and shape estimation has received
relatively little attention in the skinned multi-person linear model (SMPL)-based human …