3D facial geometry analysis and estimation using embedded optical sensors on smart eyewear

Nao Asano, Yuta Sugiura, Katsutoshi Masai, Maki Sugimoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Facial performance capture is used for animation production that projects a performer's facial expression to a computer graphics model. Retro-reflective markers and cameras are widely used for the performance capture. To capture expressions, we need to place markers on the performer's face and calibrate the intrinsic and extrinsic parameters of cameras in advance. However, the measurable space is limited to the calibrated area. In this study, we propose a system to capture facial performance using a smart eyewear with photo-reflective sensors and machine learning technique. Also, we show a result of principal components analysis of facial geometry to determine a good estimation parameter set.

LanguageEnglish
Title of host publicationACM SIGGRAPH 2018 Posters, SIGGRAPH 2018
PublisherAssociation for Computing Machinery, Inc
ISBN (Print)9781450358170
DOIs
Publication statusPublished - 2018 Aug 12
EventACM SIGGRAPH 2018 Posters - International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2018 - Vancouver, Canada
Duration: 2018 Aug 122018 Aug 16

Other

OtherACM SIGGRAPH 2018 Posters - International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2018
CountryCanada
CityVancouver
Period18/8/1218/8/16

Fingerprint

Optical sensors
Cameras
Geometry
Computer graphics
Animation
Parameter estimation
Principal component analysis
Learning systems
Sensors

Keywords

  • Facial Performance Capture
  • Wearable Device

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

Cite this

Asano, N., Sugiura, Y., Masai, K., & Sugimoto, M. (2018). 3D facial geometry analysis and estimation using embedded optical sensors on smart eyewear. In ACM SIGGRAPH 2018 Posters, SIGGRAPH 2018 [a45] Association for Computing Machinery, Inc. https://doi.org/10.1145/3230744.3230812

3D facial geometry analysis and estimation using embedded optical sensors on smart eyewear. / Asano, Nao; Sugiura, Yuta; Masai, Katsutoshi; Sugimoto, Maki.

ACM SIGGRAPH 2018 Posters, SIGGRAPH 2018. Association for Computing Machinery, Inc, 2018. a45.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Asano, N, Sugiura, Y, Masai, K & Sugimoto, M 2018, 3D facial geometry analysis and estimation using embedded optical sensors on smart eyewear. in ACM SIGGRAPH 2018 Posters, SIGGRAPH 2018., a45, Association for Computing Machinery, Inc, ACM SIGGRAPH 2018 Posters - International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2018, Vancouver, Canada, 18/8/12. https://doi.org/10.1145/3230744.3230812
Asano N, Sugiura Y, Masai K, Sugimoto M. 3D facial geometry analysis and estimation using embedded optical sensors on smart eyewear. In ACM SIGGRAPH 2018 Posters, SIGGRAPH 2018. Association for Computing Machinery, Inc. 2018. a45 https://doi.org/10.1145/3230744.3230812
Asano, Nao ; Sugiura, Yuta ; Masai, Katsutoshi ; Sugimoto, Maki. / 3D facial geometry analysis and estimation using embedded optical sensors on smart eyewear. ACM SIGGRAPH 2018 Posters, SIGGRAPH 2018. Association for Computing Machinery, Inc, 2018.
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