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X2Face: A network for controlling face generation using images, audio, and pose codes

Abstract:

The objective of this paper is a neural network model that controls the pose and expression of a given face, using another face or modality (e.g. audio). This model can then be used for lightweight, sophisticated video and image editing. We make the following three contributions. First, we introduce a network, X2Face, that can control a source face (specified by one or more frames) using another face in a driving frame to produce a generated frame with the identity of the source frame but th...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-3-030-01261-8_41

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-8945-8573
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Name:
Engineering & Physical Sciences Research Council
Grant:
EP/M013774/1
Publisher:
Springer
Series:
Lecture Notes in Computer Science
Series number:
11217
Pages:
690-706
Publication date:
2018-10-06
Acceptance date:
2018-07-27
Event title:
15th European Conference on Computer Vision (ECCV 2018)
Event location:
Munich, Germany
Event website:
https://eccv2018.org/
Event start date:
2018-09-08
Event end date:
2018-09-14
DOI:
EISBN:
9783030012618
ISBN:
9783030012601
Language:
English
Keywords:
Pubs id:
1158318
Local pid:
pubs:1158318
Deposit date:
2021-01-25

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