Estimating the pose of a human in 3D given an image or a video has recently received
significant attention from the scientific community. The main reasons for this trend are the …

This survey reviews advances in human motion capture and analysis from 2000 to 2006,
following a previous survey of papers up to 2000 [TB Moeslund, E. Granum, A survey of …

Following the success of deep convolutional networks, state-of-the-art methods for 3d
human pose estimation have focused on deep end-to-end systems that predict 3d joint …

We explore 3D human pose estimation from a single RGB image. While many approaches
try to directly predict 3D pose from image measurements, we explore a simple architecture …

In this work, we address the problem of 3D human pose estimation from a sequence of 2D
human poses. Although the recent success of deep networks has led many state-of-the-art …

A human pose is naturally represented as a graph where the joints are the nodes and the
bones are the edges. So it is natural to apply Graph Convolutional Network (GCN) to …

Deep learning revolutionized data science, and recently its popularity has grown
exponentially, as did the amount of papers employing deep networks. Vision tasks, such as …

We propose an end-to-end architecture for joint 2D and 3D human pose estimation in
natural images. Key to our approach is the generation and scoring of a number of pose …

This paper addresses the problem of 3D human pose estimation from a single image. We
follow a standard two-step pipeline by first detecting the 2D position of the N body joints, and …

Graph convolutional networks (GCNs) have been applied to 3D human pose estimation
(HPE) from 2D body joint detections and have shown encouraging performance. One …