Convolutional Pose Machines - Robotics Institute Carnegie Mellon University

Convolutional Pose Machines

Conference Paper, Proceedings of (CVPR) Computer Vision and Pattern Recognition, pp. 4724 - 4732, June, 2016

Abstract

Pose Machines provide a sequential prediction framework for learning rich implicit spatial models. In this work we show a systematic design for how convolutional networks can be incorporated into the pose machine framework for learning image features and image-dependent spatial models for the task of pose estimation. The contribution of this paper is to implicitly model long-range dependencies between variables in structured prediction tasks such as articulated pose estimation. We achieve this by designing a sequential architecture composed of convolutional networks that directly operate on belief maps from previous stages, producing increasingly refined estimates for part locations, without the need for explicit graphical model-style inference. Our approach addresses the characteristic difficulty of vanishing gradients during training by providing a natural learning objective function that enforces intermediate supervision, thereby replenishing back-propagated gradients and conditioning the learning procedure. We demonstrate state-of-the-art performance and outperform competing methods on standard benchmarks including the MPII, LSP, and FLIC datasets.

BibTeX

@conference{Wei-2016-122187,
author = {Shih-En Wei and Varun Ramakrishna and Takeo Kanade and Yaser Sheikh},
title = {Convolutional Pose Machines},
booktitle = {Proceedings of (CVPR) Computer Vision and Pattern Recognition},
year = {2016},
month = {June},
pages = {4724 - 4732},
}