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

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Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9004))

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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.

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Acknowledgement

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

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Authors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong

    Sijin Li & Antoni B. Chan

Authors
  1. Sijin Li
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  2. Antoni B. Chan
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Corresponding author

Correspondence to Sijin Li .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Li, S., Chan, A.B. (2015). 3D Human Pose Estimation from Monocular Images with Deep Convolutional Neural Network. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9004. Springer, Cham. https://doi.org/10.1007/978-3-319-16808-1_23

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  • DOI: https://doi.org/10.1007/978-3-319-16808-1_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16807-4

  • Online ISBN: 978-3-319-16808-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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