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abstract

UBeBot: voice-driven, personalized, avatar-based communicative video content in A/R

Published:28 July 2019Publication History

ABSTRACT

UBeBot allows a mobile user to create a 3D avatar of themselves using a photo, as well as dress and style the avatar. Users then record their voice, allowing the avatar to act our the content of the utterance, including lip sync, facial expressions, gestures and other body language. This animated performance is generated automatically by analyzing the recorded voice signal, and does not require any camera tracking. The 3D avatar can then be placed in augmented reality (A/R) and saved to a video for sharing on social media. A mobile user is thus able to create their own personalized, animated and voiced 3D A/R content. Performances can be saved and triggered to create the appearance of interactive conversations.

References

  1. Stacy Marsella, Yuyu Xu, Margaux Lhommet, Andrew Feng, Stefan Scherer, and Ari Shapiro. 2013. Virtual Character Performance from Speech. In Proceedings of the 12th ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA '13). ACM, New York, NY, USA, 25--35. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. UBeBot: voice-driven, personalized, avatar-based communicative video content in A/R

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    • Published in

      cover image ACM Conferences
      SIGGRAPH '19: ACM SIGGRAPH 2019 Appy Hour
      July 2019
      20 pages
      ISBN:9781450363068
      DOI:10.1145/3305365

      Copyright © 2019 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: 28 July 2019

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

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