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abstract

Forward Selfies

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Published:06 August 2021Publication History

ABSTRACT

Taking selfies is a common practice for smartphone users. Simultaneously capturing oneself and the desired background is not a trivial task, because it is often not possible to get a good view of both. Moreover, users often loose attention of their surroundings, thus taking a selfie also showed to lead to serious injuries. To ease the process of capturing selfies and to make it more safe, this work proposes forward selfies as a simple yet effective concept to account for both, risk and challenges. Forward selfies seamlessly combine images of the front-facing and the rear-facing smartphone camera. We propose a mobile app that builds on this concept and implements the selfie synthesis in a post-processing image composition stage. Thereby, we can take advantage of the commonly more advanced back-camera hardware, i.e., providing higher image resolutions, larger field of views, and different perspectives. Finally, we leverage built-in camera optimizations for independently (de-)focusing objects at different distances, such as for persons and backgrounds. We conclude that the concept of forward selfies can effectively address and solve certain challenges of capturing selfies, which we demonstrate by a simple app user interface.

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References

  1. Yi-Tsung Hsieh and Mei-Chen Yeh. 2017. Head Pose Recommendation for Taking Good Selfies. In Proc. Workshop on Multimodal Understanding of Social, Affective and Subjective Attributes (Mountain View, California, USA). ACM, New York, NY, USA, 55–60. https://doi.org/10.1145/3132515.3132518Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Mahdi M. Kalayeh, Misrak Seifu, Wesna LaLanne, and Mubarak Shah. 2015. How to Take a Good Selfie?. In Proc. International Conference on Multimedia(Brisbane, Australia). ACM, New York, NY, USA, 923–926. https://doi.org/10.1145/2733373.2806365Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Zhanghan Ke, Kaican Li, Yurou Zhou, Qiuhua Wu, Xiangyu Mao, Qiong Yan, and Rynson WH Lau. 2020. Is a Green Screen Really Necessary for Real-Time Portrait Matting?arxiv:2011.11961 [cs.CV]Google ScholarGoogle Scholar
  4. Jitender Singh Virk and Abhinav Dhall. 2019. Garuda: A Deep Learning Based Solution for Capturing Selfies Safely. In Proc. International Conference on Intelligent User Interfaces: Companion (Marina del Ray, California). ACM, New York, NY, USA, 43–44. https://doi.org/10.1145/3308557.3308669Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Mei-Chen Yeh and Hsiao-Wei Lin. 2014. Virtual Portraitist: Aesthetic Evaluation of Selfies Based on Angle. In Proc. ACM International Conference on Multimedia (Orlando, Florida, USA). ACM, New York, NY, USA, 221–224. https://doi.org/10.1145/2647868.2656401Google ScholarGoogle ScholarDigital LibraryDigital Library

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  • Published in

    cover image ACM Conferences
    SIGGRAPH '21: ACM SIGGRAPH 2021 Appy Hour
    August 2021
    18 pages
    ISBN:9781450383585
    DOI:10.1145/3450415

    Copyright © 2021 Owner/Author

    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 6 August 2021

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    Overall Acceptance Rate1,822of8,601submissions,21%

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    SIGGRAPH '24

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