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Beauty eMakeup: A Deep Makeup Transfer System

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Published:01 October 2016Publication History

ABSTRACT

In this demo, we present a Beauty eMakeup System to automatically recommend the most suitable makeup for a female and synthesis the makeup on her face. Given a before-makeup face, her most suitable makeup is determined automatically. Then, both the before-makeup and the reference faces are fed into the proposed Deep Transfer Network to generate the after-makeup face. Our end-to-end makeup transfer network have several nice properties including: (1) with complete functions: including foundation, lip gloss, and eye shadow transfer; (2) cosmetic specific: different cosmetics are transferred in different manners; (3) localized: different cosmetics are applied on different facial regions; (4) producing naturally looking results without obvious artifacts; (5) controllable makeup lightness: various results from light makeup to heavy makeup can be generated. Extensive experimental evaluations and analysis on testing images well demonstrate the effectiveness of the proposed system.

References

  1. L. A. Gatys, A. S. Ecker, and M. Bethge., A neural algorithm of artistic style. CoRR, abs/1508.06576, 2015.Google ScholarGoogle Scholar
  2. S. Liu, X. Liang, L. Liu, X. Shen, J. Yang, C. Xu, L. Lin, X. Cao, and S. Yan. Matching-cnn meets knn: Quasi-parametric human parsing. In Computer Vision and Pattern Recognition, pages 1419-1427, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  3. D. Guo and T. Sim. Digital face makeup by example. In Computer Vision and Pattern Recognition, pages 73-79, 2009.Google ScholarGoogle Scholar
  4. S. Liu, X. Ou, R. Qian, W. Wang, and X. Cao. Makeup like a superstar: Deep localized makeup transfer network. 2016.Google ScholarGoogle Scholar

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  1. Beauty eMakeup: A Deep Makeup Transfer System

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      • Published in

        cover image ACM Conferences
        MM '16: Proceedings of the 24th ACM international conference on Multimedia
        October 2016
        1542 pages
        ISBN:9781450336031
        DOI:10.1145/2964284

        Copyright © 2016 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.

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 1 October 2016

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        Qualifiers

        • demonstration

        Acceptance Rates

        MM '16 Paper Acceptance Rate52of237submissions,22%Overall Acceptance Rate995of4,171submissions,24%

        Upcoming Conference

        MM '24
        MM '24: The 32nd ACM International Conference on Multimedia
        October 28 - November 1, 2024
        Melbourne , VIC , Australia

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