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 …

Highlights•Deep neural networks are shattering performance benchmarks in computer
vision for various tasks.•Using modern deep learning approaches (DNNs) in the lab is a …

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 …

The success of deep learning has been a catalyst to solving increasingly complex machine-
learning problems, which often involve multiple data modalities. We review recent advances …

Extracting behavioral measurements non-invasively from video is stymied by the fact that it is
a hard computational problem. Recent advances in deep learning have tremendously …

Pedestrian detection and tracking have become an important field in the computer vision
research area. This growing interest, started in the last decades, might be explained by the …

We describe the first method to automatically estimate the 3D pose of the human body as
well as its 3D shape from a single unconstrained image. We estimate a full 3D mesh and …

We propose a CNN-based approach for 3D human body pose estimation from single RGB
images that addresses the issue of limited generalizability of models trained solely on the …

In this paper, we propose a novel graph convolutional network architecture, Graph Stacked
Hourglass Networks, for 2D-to-3D human pose estimation tasks. The proposed architecture …

In the last few years, deep learning has led to very good performance on a variety of
problems, such as visual recognition, speech recognition and natural language processing …