abstract

Lessons from the evolution of an anatomical facial muscle model

Authors:

Lana Lan,

Matthew Cong,

Ronald FedkiwAuthors Info & Claims

DigiPro '17: Proceedings of the ACM SIGGRAPH Digital Production Symposium

Article No.: 11, Pages 1 - 3

https://doi.org/10.1145/3105692.3105693

Published: 29 July 2017 Publication History

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Abstract

Recently, Industrial Light & Magic has begun exploring facial muscle simulation as a means of augmenting our blendshape-based facial animation workflow in order to attain higher quality results. During this process, we discovered that a precise and accurate model of the underlying facial anatomy is key to obtaining high-quality facial simulation results that can be used for photorealistic hero characters. We present an overview of our workflow for developing such a model along with some of the key anatomical lessons that were essential to the process.

References

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Matthew Cong, Michael Bao, Jane L. E, Kiran S. Bhat, and Ronald Fedkiw. 2015. Fully Automatic Generation of Anatomical Face Simulation Models. In Proc. of the 14th ACM SIGGRAPH / Eurographics Symposium on Computer Animation. 175--183.

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[3]

Matthew Cong, Kiran S. Bhat, and Ronald Fedkiw. 2016. Art-directed Muscle Simulation for High-end Facial Animation. In Proc. of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation. 119--127.

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E. Sifakis, A. Selle, A. Robinson-Mosher, and R. Fedkiw. 2006. Simulating Speech with a Physics-Based Facial Muscle Model. ACM SIGGRAPH/Eurographics Symp. on Comput. Anim. (2006), 261--270.

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Cited By

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Cong MLan LFedkiw R(2024)Local Geometric Indexing of High Resolution Data for Facial Reconstruction From Sparse MarkersIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.328949530:8(5289-5298)Online publication date: Aug-2024
https://doi.org/10.1109/TVCG.2023.3289495
Srinivasan SWang QRojas JKlár GKavan LSifakis E(2021)Learning active quasistatic physics-based models from dataACM Transactions on Graphics10.1145/3450626.345988340:4(1-14)Online publication date: 19-Jul-2021
https://dl.acm.org/doi/10.1145/3450626.3459883
Hyde DBao MFedkiw R(2021)On Obtaining Sparse Semantic Solutions for Inverse Problems, Control, and Neural Network TrainingJournal of Computational Physics10.1016/j.jcp.2021.110498(110498)Online publication date: Jun-2021
https://doi.org/10.1016/j.jcp.2021.110498
Show More Cited By

Index Terms

Lessons from the evolution of an anatomical facial muscle model
1. Computing methodologies
  1. Computer graphics
    1. Animation
      1. Physical simulation

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Information

Published In

DigiPro '17: Proceedings of the ACM SIGGRAPH Digital Production Symposium

July 2017

43 pages

ISBN:9781450351027

DOI:10.1145/3105692

Conference Chairs:
Andrew Pearce
DreamWorks Animation
,
Corban Gossett
Nimble Collective
,
Editor:
Stephen N. Spencer
University of Washington
,
Program Chairs:
Christopher Horvath
Facebook
,
Cary Phillips
ILM

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

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Published: 29 July 2017

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DigiPro '17: The Digital Production Symposium

July 29, 2017

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Cong MLan LFedkiw R(2024)Local Geometric Indexing of High Resolution Data for Facial Reconstruction From Sparse MarkersIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.328949530:8(5289-5298)Online publication date: Aug-2024
https://doi.org/10.1109/TVCG.2023.3289495
Srinivasan SWang QRojas JKlár GKavan LSifakis E(2021)Learning active quasistatic physics-based models from dataACM Transactions on Graphics10.1145/3450626.345988340:4(1-14)Online publication date: 19-Jul-2021
https://dl.acm.org/doi/10.1145/3450626.3459883
Hyde DBao MFedkiw R(2021)On Obtaining Sparse Semantic Solutions for Inverse Problems, Control, and Neural Network TrainingJournal of Computational Physics10.1016/j.jcp.2021.110498(110498)Online publication date: Jun-2021
https://doi.org/10.1016/j.jcp.2021.110498
Kadleček PKavan L(2019)Building Accurate Physics-based Face Models from DataProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/33402562:2(1-16)Online publication date: 26-Jul-2019
https://dl.acm.org/doi/10.1145/3340256
Cong MFedkiw R(2019)Muscle-based facial retargeting with anatomical constraintsACM SIGGRAPH 2019 Talks10.1145/3306307.3328154(1-2)Online publication date: 28-Jul-2019
https://dl.acm.org/doi/10.1145/3306307.3328154
Bao MCong MGrabli SFedkiw R(2019)High-Quality Face Capture Using Anatomical Muscles2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR.2019.01106(10794-10803)Online publication date: Jun-2019
https://doi.org/10.1109/CVPR.2019.01106
Yang WMarshak NSýkora DRamalingam SKavan L(2019)Building anatomically realistic jaw kinematics model from dataThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-019-01677-835:6-8(1105-1118)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s00371-019-01677-8
Dinev DBeeler TBradley DBächer MXu HKavan L(2018)User‐Guided Lip Correction for Facial Performance CaptureComputer Graphics Forum10.1111/cgf.1351537:8(93-101)Online publication date: 12-Sep-2018
https://doi.org/10.1111/cgf.13515

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Muscle simulation for facial animation in Kong: Skull Island

Muscle-based facial retargeting with anatomical constraints

Customizing blendshapes to capture facial details

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