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Automatic Camera Path Generation from 360\(^\circ \) Video

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Advances in Visual Computing (ISVC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11844))

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Abstract

Omnidirectional (\(360^\circ \)) video is a novel media format, rapidly becoming adopted in media production and consumption as part of today’s ongoing virtual reality revolution. The goal of automatic camera path generation is to calculate automatically a visually interesting camera path from a \(360^\circ \) video in order to provide a traditional, TV-like consumption experience. In this work, we describe our algorithm for automatic camera path generation, based on extraction of the information of the scene objects with deep learning based methods.

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Notes

  1. 1.

    https://stephenfollows.com/many-shots-average-movie/.

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Acknowledgment

This work has received funding from the European Union’s Horizon 2020 research and innovation programme, grant n\(^\circ \) 761934, Hyper360 (“Enriching 360 media with 3D storytelling and personalisation elements”). Thanks to Rundfunk Berlin-Brandenburg (RBB) for providing the 360\(^\circ \) video content.

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Authors and Affiliations

  1. DIGITAL - Institute for Information and Communication Technologies, JOANNEUM RESEARCH, Graz, Austria

    Hannes Fassold

Authors
  1. Hannes Fassold
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Corresponding author

Correspondence to Hannes Fassold .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. University of Nevada, Reno, NV, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Daniela Ushizima

  6. Latent AI, Palo Alto, CA, USA

    Sek Chai

  7. Texas A&M University, College Station, TX, USA

    Shinjiro Sueda

  8. Louisiana State University, Baton Rouge, LA, USA

    Xin Lin

  9. University of North Carolina at Charlotte, Charlotte, NC, USA

    Aidong Lu

  10. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Daniel Thalmann

  11. Notre Dame University, Notre Dame, IN, USA

    Chaoli Wang

  12. Bosch Research North America, Palo Alto, CA, USA

    Panpan Xu

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Fassold, H. (2019). Automatic Camera Path Generation from 360\(^\circ \) Video. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11844. Springer, Cham. https://doi.org/10.1007/978-3-030-33720-9_39

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  • DOI: https://doi.org/10.1007/978-3-030-33720-9_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33719-3

  • Online ISBN: 978-3-030-33720-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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