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3D Human Pose Estimation from Monocular Images with Deep Convolutional Neural Network

  • Sijin LiEmail author
  • Antoni B. Chan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9004)

Abstract

In this paper, we propose a deep convolutional neural network for 3D human pose estimation from monocular images. We train the network using two strategies: (1) a multi-task framework that jointly trains pose regression and body part detectors; (2) a pre-training strategy where the pose regressor is initialized using a network trained for body part detection. We compare our network on a large data set and achieve significant improvement over baseline methods. Human pose estimation is a structured prediction problem, i.e., the locations of each body part are highly correlated. Although we do not add constraints about the correlations between body parts to the network, we empirically show that the network has disentangled the dependencies among different body parts, and learned their correlations.

Keywords

Detection Task Convolutional Neural Network Deep Neural Network Joint Point Regression Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work was supported by the Research Grants Council of the Hong Kong Special Administrative Region, China (CityU 123212 and CityU 110513).

Supplementary material

Supplementary material (mov 27,852 KB)

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceCity University of Hong KongKowloon TongHong Kong

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