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 the pose of multiple animals is a challenging computer vision problem: frequent
interactions cause occlusions and complicate the association of detected keypoints to the …

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 …

Realtime multi-person 2D pose estimation is a key component in enabling machines to have
an understanding of people in images and videos. In this work, we present a realtime …

Noninvasive behavioral tracking of animals during experiments is critical to many scientific
pursuits. Extracting the poses of animals without using markers is often essential to …

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 …

Quantifying behavior is crucial for many applications in neuroscience. Videography provides
easy methods for the observation and recording of animal behavior in diverse settings, yet …

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 …

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 …