We propose a method for human pose estimation based on Deep Neural Networks (DNNs).
The pose estimation is formulated as a DNN-based regression problem towards body joints …

This paper proposes a new hybrid architecture that consists of a deep Convolutional
Network and a Markov Random Field. We show how this architecture is successfully applied …

Recovering human pose from RGB images and videos has drawn increasing attention in
recent years owing to minimum sensor requirements and applicability in diverse fields such …

Video-based human pose recovery is usually conducted by retrieving relevant poses using
image features. In the retrieving process, the map** between 2D images and 3D poses is …

We describe a method for articulated human detection and human pose estimation in static
images based on a new representation of deformable part models. Rather than modeling …

We propose a new method to quickly and accurately predict 3D positions of body joints from
a single depth image, using no temporal information. We take an object recognition …

We propose a new method to quickly and accurately predict human pose---the 3D positions
of body joints---from a single depth image, without depending on information from preceding …

Most recent approaches to monocular 3D human pose estimation rely on Deep Learning.
They typically involve regressing from an image to either 3D joint coordinates directly or 2D …

Human 3D pose estimation from a single image is a challenging task with numerous
applications. Convolutional Neural Networks (CNNs) have recently achieved superior …

We describe two new approaches to human pose estimation. Both can quickly and
accurately predict the 3D positions of body joints from a single depth image without using …