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 …

Human pose estimation aims at predicting the poses of human body parts in images or
videos. Since pose motions are often driven by some specific human actions, knowing the …

We show, for the first time, that neural networks trained only on synthetic data achieve state-
of-the-art accuracy on the problem of 3D human pose and shape (HPS) estimation from real …

In this paper, we are interested in the human pose estimation problem with a focus on
learning reliable high-resolution representations. Most existing methods recover high …

Vision-based monocular human pose estimation, as one of the most fundamental and
challenging problems in computer vision, aims to obtain posture of the human body from …

This paper investigates the task of 2D human whole-body pose estimation, which aims to
localize dense landmarks on the entire human body including face, hands, body, and feet …

Quantitative behavioral measurements are important for answering questions across
scientific disciplines—from neuroscience to ecology. State-of-the-art deep-learning methods …

Existing systems for video-based pose estimation and tracking struggle to perform well on
realistic videos with multiple people and often fail to output body-pose trajectories consistent …

Many image-based perception tasks can be formulated as detecting, associating and
tracking semantic keypoints, eg, human body pose estimation and tracking. In this work, we …

Automatic emotion recognition has become a trending research topic in the past decade.
While works based on facial expressions or speech abound, recognizing affect from body …