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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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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. 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.
Item Description: ICPRAI 2022. Lecture Notes in Computer Science, vol 13364..
College: Faculty of Science and Engineering
Start Page: 246
End Page: 257

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