Introduction The earliest works in action recognition focused on tracking body parts and classifying the joint movements. These pose-based approaches, while straight-forward, require accurate tracking of body parts, which is a challenging task in its own right. As recent trends in action recognition have shifted towards natural and unconstrained videos (e.g. films, broadcast sports, Youtube vid...

of “Shape Models of the Human Body for Distributed Inference” by Silvia Zuffi, Ph.D., Brown University, May 2015 In this thesis we address the problem of building shape models of the human body, in 2D and 3D, which are realistic and efficient to use. We focus our efforts on the human body, which is highly articulated and has interesting shape variations, but the approaches we present here can b...

In this paper, we propose a knowledge-guided pose grammar network to tackle the problem of 3D human pose estimation. Our model directly takes 2D poses as inputs and learns the generalized 2D-3D mapping function, which renders high applicability. The proposed network consists of a base network which efficiently captures pose-aligned features and a hierarchy of Bidirectional RNNs on top of it to ...

Pictorial structure models are the de facto standard for 2D human pose estimation. Numerous refinements and improvements have been proposed such as discriminatively trained body part detectors, flexible body models, and local and global mixtures. While these techniques allow to achieve state-of-the-art performance for 2D pose estimation, they have not yet been extended to enable pose estimation...

The goal of this work is fully automatic 2D human pose estimation in unconstrained TV shows and feature films. Direct pose estimation on this uncontrolled material is often too difficult, especially when knowing nothing about the location, scale, pose, and appearance of the person, or even whether there is a person in the frame or not. We propose an approach that progressively reduces the searc...