Show simple item record

dc.contributor.author
Chandran, Prashanth
dc.contributor.author
Bradley, Derek
dc.contributor.author
Gross, Markus
dc.contributor.author
Beeler, Thabo
dc.date.accessioned
2021-08-11T12:53:59Z
dc.date.available
2021-01-26T13:46:56Z
dc.date.available
2021-01-26T14:11:04Z
dc.date.available
2021-08-11T12:53:59Z
dc.date.issued
2020-11
dc.identifier.isbn
978-1-7281-8128-8
en_US
dc.identifier.other
10.1109/3DV50981.2020.00044
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/465688
dc.identifier.doi
10.3929/ethz-b-000465688
dc.description.abstract
Face models built from 3D face databases are often used in computer vision and graphics tasks such as face reconstruction, replacement, tracking and manipulation. For such tasks, commonly used multi-linear morphable models, which provide semantic control over facial identity and expression, often lack quality and expressivity due to their linear nature. Deep neural networks offer the possibility of non-linear face modeling, where so far most research has focused on generating realistic facial images with less focus on 3D geometry, and methods that do produce geometry have little or no notion of semantic control, thereby limiting their artistic applicability. We present a method for nonlinear 3D face modeling using neural architectures that provides intuitive semantic control over both identity and expression by disentangling these dimensions from each other, essentially combining the benefits of both multi-linear face models and nonlinear deep face networks. The result is a powerful, semantically controllable, nonlinear, parametric face model. We demonstrate the value of our semantic deep face model with applications of 3D face synthesis, facial performance transfer, performance editing, and 2D landmark-based performance retargeting.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
Face Modelling
en_US
dc.subject
Morphable models
en_US
dc.subject
deep learning
en_US
dc.subject
appearance synthesis
en_US
dc.subject
super resolution
en_US
dc.subject
Face tracking
en_US
dc.subject
Retargeting
en_US
dc.subject
Artist Control
en_US
dc.title
Semantic Deep Face Models
en_US
dc.type
Conference Paper
dc.rights.license
In Copyright - Non-Commercial Use Permitted
ethz.book.title
2020 International Conference on 3D Vision (3DV)
en_US
ethz.pages.start
345
en_US
ethz.pages.end
354
en_US
ethz.size
10 p. accepted version
en_US
ethz.version.deposit
acceptedVersion
en_US
ethz.event
8th International Conference on 3D Vision (3DV 2020) (virtual)
en_US
ethz.event.location
Fukuoka, Japan
en_US
ethz.event.date
November 25–28, 2020
en_US
ethz.notes
Conference lecture held at poster presentation on November 27, 2020. Due to the Coronavirus (COVID-19) the conference was conducted virtually.
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Piscataway, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02659 - Institut für Visual Computing / Institute for Visual Computing::03420 - Gross, Markus / Gross, Markus
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02659 - Institut für Visual Computing / Institute for Visual Computing::03420 - Gross, Markus / Gross, Markus
en_US
ethz.date.deposited
2021-01-26T13:47:04Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-01-26T14:11:13Z
ethz.rosetta.lastUpdated
2022-03-29T11:01:15Z
ethz.rosetta.versionExported
true
ethz.COinS
ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.atitle=Semantic%20Deep%20Face%20Models&rft.date=2020-11&rft.spage=345&rft.epage=354&rft.au=Chandran,%20Prashanth&Bradley,%20Derek&Gross,%20Markus&Beeler,%20Thabo&rft.isbn=978-1-7281-8128-8&rft.genre=proceeding&rft_id=info:doi/10.1109/3DV50981.2020.00044&rft.btitle=2020%20International%20Conference%20on%203D%20Vision%20(3DV)
 Search print copy at ETH Library

Files in this item

Thumbnail

Publication type

Show simple item record