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 …

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 …

Given an image with multiple people, our goal is to directly regress the pose and shape of all
the people as well as their relative depth. Inferring the depth of a person in an image …

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 …

We propose a method to reconstruct global human trajectories from videos in the wild. Our
optimization method decouples the camera and human motion, which allows us to place …

This paper focuses on the regression of multiple 3D people from a single RGB image.
Existing approaches predominantly follow a multi-stage pipeline that first detects people in …

We present a statistical, articulated 3D human shape modeling pipeline, within a fully
trainable, modular, deep learning framework. Given high-resolution complete 3D body …

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 …

Estimation of the human pose from a monocular camera has been an emerging research
topic in the computer vision community with many applications. Recently, benefiting from the …

Multi-person pose estimation is fundamental to many computer vision tasks and has made
significant progress in recent years. However, few previous methods explored the problem …