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Fully Automatic Multi-person Human Motion Capture for VR Applications

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Virtual Reality and Augmented Reality (EuroVR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11162))

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Abstract

Fully automatic tracking of articulated motion in real-time with monocular RGB camera is a challenging problem which is essential for many virtual reality (VR) applications. In this paper, we propose a novel temporally stable solution for this problem which can be directly employed in VR practical applications. Our algorithm automatically estimates the number of persons in the scene, generates their corresponding person specific 3D skeletons, and estimates their initial 3D locations. For every frame, it fits each 3D skeleton to the corresponding 2D body-parts locations which are estimated with one of the existing CNN-based 2D pose estimation methods. The 3D pose of every person is estimated by maximizing an objective function that combines a skeleton fitting term with motion and pose priors. Our algorithm detects persons who enter or leave the scene, and dynamically generates or deletes their 3D skeletons. This makes our algorithm the first monocular RGB method usable in real-time applications such as dynamically including multiple persons in a virtual environment using the camera of the VR-headset. We show that our algorithm is applicable for tracking multiple persons in outdoor scenes, community videos and low quality videos captured with mobile-phone cameras.

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Acknowledgements

This work has been partially funded by the Federal Ministry of Education and Research of the Federal Republic of Germany as part of the research projects DYNAMICS (Grant number 01IW15003) and VIDETE (Grant number 01IW18002).

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Authors and Affiliations

  1. German Research Centre for Artificial Intelligence (DFKI), Kaiserslautern, Germany

    Ahmed Elhayek, Onorina Kovalenko, Pramod Murthy, Jameel Malik & Didier Stricker

  2. University of Kaiserslautern, Kaiserslautern, Germany

    Ahmed Elhayek, Pramod Murthy, Jameel Malik & Didier Stricker

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  1. Ahmed Elhayek
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  2. Onorina Kovalenko
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  3. Pramod Murthy
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  4. Jameel Malik
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  5. Didier Stricker
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Correspondence to Ahmed Elhayek , Onorina Kovalenko , Pramod Murthy , Jameel Malik or Didier Stricker .

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Editors and Affiliations

  1. University of Paris-Sud, Orsay, France

    Patrick Bourdot

  2. University of Nottingham, Nottingham, UK

    Sue Cobb

  3. University of Minnesota, Minneapolis, MN, USA

    Victoria Interrante

  4. Nara Institute of Science and Technology, Ikoma, Japan

    Hirokazu kato

  5. University of Kaiserslautern and DFKI, Kaiserslautern, Germany

    Didier Stricker

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Elhayek, A., Kovalenko, O., Murthy, P., Malik, J., Stricker, D. (2018). Fully Automatic Multi-person Human Motion Capture for VR Applications. In: Bourdot, P., Cobb, S., Interrante, V., kato, H., Stricker, D. (eds) Virtual Reality and Augmented Reality. EuroVR 2018. Lecture Notes in Computer Science(), vol 11162. Springer, Cham. https://doi.org/10.1007/978-3-030-01790-3_3

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