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Hair Emoting with Style Guides in Turning Red

Published:24 July 2022Publication History

ABSTRACT

For Pixar’s feature film Turning Red, the grooming and simulation teams faced the challenge of handling characters with millions of fur and hair curves, which often needed to behave differently in each shot reflecting the characters’ emotional states. This work describes new tools developed to assist artists in managing and sculpting these large amounts of fur and hair. In particular, we present a novel surface-aware technique for curve deformation that interpolates hair sculpts at varying levels of detail, accompanied by a customized user interface for interactively browsing hair layers.

References

  1. A. Butts, B. Porter, D. Van Gelder, M. Hessler, V. Krishna, and G. Monheit. 2018. Engineering Full-Fidelity Hair for Incredibles 2. In ACM SIGGRAPH Talks. Article 14, 2 pages.Google ScholarGoogle Scholar
  2. F. de Goes, A. Butts, and M. Desbrun. 2020. Discrete Differential Operators on Polygonal Meshes. ACM Trans. Graph. 39, 4, Article 110 (jul 2020), 14 pages.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. H. Iben, J. Brooks, and C. Bolwyn. 2019. Holding the Shape in Hair Simulation. In ACM SIGGRAPH Talks. Article 59, 2 pages.Google ScholarGoogle Scholar

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  • Published in

    cover image ACM Conferences
    SIGGRAPH '22: ACM SIGGRAPH 2022 Talks
    July 2022
    108 pages
    ISBN:9781450393713
    DOI:10.1145/3532836

    Copyright © 2022 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: 24 July 2022

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