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One-Shot Decoupled Face Reenactment with Vision Transformer

Chen Hu, Xianghua Xie Orcid Logo

Pattern Recognition and Artificial Intelligence, Volume: Lecture Notes in Computer Science (LNCS, volume 13364), Pages: 246 - 257

Swansea University Authors: Chen Hu, Xianghua Xie Orcid Logo

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DOI (Published version): 10.1007/978-3-031-09282-4_21

Abstract

Recent face reenactment paradigm involves estimating an optical flow to warp the source image or its feature maps such that pixel values can be sampled to generate the reenacted image. We propose a one-shot framework in which the reenactment of the overall face and individual landmarks are decoupled...

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Published in: Pattern Recognition and Artificial Intelligence
ISBN: 9783031092817 9783031092824
ISSN: 0302-9743 1611-3349
Published: Cham Springer International Publishing 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa59668
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spelling 2023-01-04T14:42:31.6565356 v2 59668 2022-03-18 One-Shot Decoupled Face Reenactment with Vision Transformer 55d3ba5f8378c2e3439d7e3962aee726 Chen Hu Chen Hu true false b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2022-03-18 SCS Recent face reenactment paradigm involves estimating an optical flow to warp the source image or its feature maps such that pixel values can be sampled to generate the reenacted image. We propose a one-shot framework in which the reenactment of the overall face and individual landmarks are decoupled. We show that a shallow Vision Transformer can effectively estimate optical flow without much parameters and training data. When reenacting different identities, our method remedies previous conditional generator based method’s inability to preserve identities in reenacted images. To address the identity preserving problem in face reenactment, we model landmark coordinate transformation as a style transfer problem, yielding further improvement on preserving the source image’s identity in the reenacted image. Our method achieves the lower head pose error on the CelebV dataset while obtaining competitive results in identity preserving and expression accuracy. Conference Paper/Proceeding/Abstract Pattern Recognition and Artificial Intelligence Lecture Notes in Computer Science (LNCS, volume 13364) 246 257 Springer International Publishing Cham 9783031092817 9783031092824 0302-9743 1611-3349 29 5 2022 2022-05-29 10.1007/978-3-031-09282-4_21 ICPRAI 2022. Lecture Notes in Computer Science, vol 13364.. COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2023-01-04T14:42:31.6565356 2022-03-18T11:33:50.3894363 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Chen Hu 1 Xianghua Xie 0000-0002-2701-8660 2 Under embargo Under embargo 2022-08-02T10:23:23.7365840 Output 678846 application/pdf Accepted Manuscript true 2023-05-29T00:00:00.0000000 true eng
title One-Shot Decoupled Face Reenactment with Vision Transformer
spellingShingle One-Shot Decoupled Face Reenactment with Vision Transformer
Chen Hu
Xianghua Xie
title_short One-Shot Decoupled Face Reenactment with Vision Transformer
title_full One-Shot Decoupled Face Reenactment with Vision Transformer
title_fullStr One-Shot Decoupled Face Reenactment with Vision Transformer
title_full_unstemmed One-Shot Decoupled Face Reenactment with Vision Transformer
title_sort One-Shot Decoupled Face Reenactment with Vision Transformer
author_id_str_mv 55d3ba5f8378c2e3439d7e3962aee726
b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv 55d3ba5f8378c2e3439d7e3962aee726_***_Chen Hu
b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
author Chen Hu
Xianghua Xie
author2 Chen Hu
Xianghua Xie
format Conference Paper/Proceeding/Abstract
container_title Pattern Recognition and Artificial Intelligence
container_volume Lecture Notes in Computer Science (LNCS, volume 13364)
container_start_page 246
publishDate 2022
institution Swansea University
isbn 9783031092817
9783031092824
issn 0302-9743
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doi_str_mv 10.1007/978-3-031-09282-4_21
publisher Springer International Publishing
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description Recent face reenactment paradigm involves estimating an optical flow to warp the source image or its feature maps such that pixel values can be sampled to generate the reenacted image. We propose a one-shot framework in which the reenactment of the overall face and individual landmarks are decoupled. We show that a shallow Vision Transformer can effectively estimate optical flow without much parameters and training data. When reenacting different identities, our method remedies previous conditional generator based method’s inability to preserve identities in reenacted images. To address the identity preserving problem in face reenactment, we model landmark coordinate transformation as a style transfer problem, yielding further improvement on preserving the source image’s identity in the reenacted image. Our method achieves the lower head pose error on the CelebV dataset while obtaining competitive results in identity preserving and expression accuracy.
published_date 2022-05-29T04:17:09Z
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score 11.012924

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