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Facial performance synthesis using deformation-driven polynomial displacement maps

Published: 01 December 2008 Publication History

Abstract

We present a novel method for acquisition, modeling, compression, and synthesis of realistic facial deformations using polynomial displacement maps. Our method consists of an analysis phase where the relationship between motion capture markers and detailed facial geometry is inferred, and a synthesis phase where novel detailed animated facial geometry is driven solely by a sparse set of motion capture markers. For analysis, we record the actor wearing facial markers while performing a set of training expression clips. We capture real-time high-resolution facial deformations, including dynamic wrinkle and pore detail, using interleaved structured light 3D scanning and photometric stereo. Next, we compute displacements between a neutral mesh driven by the motion capture markers and the high-resolution captured expressions. These geometric displacements are stored in a polynomial displacement map which is parameterized according to the local deformations of the motion capture dots. For synthesis, we drive the polynomial displacement map with new motion capture data. This allows the recreation of large-scale muscle deformation, medium and fine wrinkles, and dynamic skin pore detail. Applications include the compression of existing performance data and the synthesis of new performances. Our technique is independent of the underlying geometry capture system and can be used to automatically generate high-frequency wrinkle and pore details on top of many existing facial animation systems.

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References

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Cited By

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  • (2022)Compact Facial Landmark Layouts for Performance CaptureComputer Graphics Forum10.1111/cgf.1446341:2(121-133)Online publication date: 24-May-2022
  • (2022)Plausible 3D Face Wrinkle Generation Using Variational AutoencodersIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.305125128:9(3113-3125)Online publication date: 1-Sep-2022
  • (2018)Three-Dimensional Reconstruction from Single Image Base on Combination of CNN and Multi-Spectral Photometric StereoSensors10.3390/s1803076418:3(764)Online publication date: 2-Mar-2018
  • Show More Cited By

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Published In

cover image ACM Conferences
SIGGRAPH Asia '08: ACM SIGGRAPH Asia 2008 papers
December 2008
581 pages
ISBN:9781450318310
DOI:10.1145/1457515
  • Editor:
  • John C. Hart
Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 01 December 2008

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Author Tags

  1. facial performance synthesis
  2. polynomial displacement maps

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SIGGRAPH Asia '08 Paper Acceptance Rate 59 of 320 submissions, 18%;
Overall Acceptance Rate 178 of 869 submissions, 20%

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Cited By

View all
  • (2022)Compact Facial Landmark Layouts for Performance CaptureComputer Graphics Forum10.1111/cgf.1446341:2(121-133)Online publication date: 24-May-2022
  • (2022)Plausible 3D Face Wrinkle Generation Using Variational AutoencodersIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.305125128:9(3113-3125)Online publication date: 1-Sep-2022
  • (2018)Three-Dimensional Reconstruction from Single Image Base on Combination of CNN and Multi-Spectral Photometric StereoSensors10.3390/s1803076418:3(764)Online publication date: 2-Mar-2018
  • (2018)High-fidelity facial reflectance and geometry inference from an unconstrained imageACM Transactions on Graphics10.1145/3197517.320136437:4(1-14)Online publication date: 30-Jul-2018
  • (2018)Mesoscopic Facial Geometry Inference Using Deep Neural Networks2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition10.1109/CVPR.2018.00877(8407-8416)Online publication date: Jun-2018
  • (2017)Makeup LampsComputer Graphics Forum10.5555/3128975.312900436:2(311-323)Online publication date: 1-May-2017
  • (2017)Makeup Lamps: Live Augmentation of Human Faces via ProjectionComputer Graphics Forum10.1111/cgf.1312836:2(311-323)Online publication date: 23-May-2017
  • (2016)BlendForcesProceedings of the 37th Annual Conference of the European Association for Computer Graphics10.5555/3058909.3058955(341-352)Online publication date: 9-May-2016
  • (2016)BlendForces: A Dynamic Framework for Facial AnimationComputer Graphics Forum10.1111/cgf.1283635:2(341-352)Online publication date: 27-May-2016
  • (2016)Real-time facial expression transfer with single video cameraComputer Animation and Virtual Worlds10.1002/cav.169727:3-4(301-310)Online publication date: 1-May-2016
  • Show More Cited By

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