Article
Authors: Li-Chen Cheng, Shu-Chuan Hsu, Pin-Hua Lee, Hsiu-Chieh Lee, + 3, Che-Hsien Lin, Jun-Cheng Chen, Chih-Yu Wang (Less)
Computer Vision – ACCV 2022: 16th Asian Conference on Computer Vision, Macao, China, December 4–8, 2022, Proceedings, Part IV
Pages 105 - 120
Published: 02 March 2023 Publication History
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Abstract
High-fidelity kinship face synthesis is a challenging task due to the limited amount of kinship data available for training and low-quality images. In addition, it is also hard to trace the genetic traits between parents and children from those low-quality training images. To address these issues, we leverage the pre-trained state-of-the-art face synthesis model, StyleGAN2, for kinship face synthesis. To handle large age, gender and other attribute variations between the parents and their children, we conduct a thorough study of its rich latent spaces and different encoder architectures for an optimized encoder design to repurpose StyleGAN2 for kinship face synthesis. The obtained latent representation from our developed encoder pipeline with stage-wise training strikes a better balance of editability and synthesis fidelity for identity preserving and attribute manipulations than other compared approaches. With extensive subjective, quantitative, and qualitative evaluations, the proposed approach consistently achieves better performance in terms of facial attribute heredity and image generation fidelity than other compared state-of-the-art methods. This demonstrates the effectiveness of the proposed approach which can yield promising and satisfactory kinship face synthesis using only a single and straightforward encoder architecture.
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Index Terms
KinStyle: A Strong Baseline Photorealistic Kinship Face Synthesis withanOptimized StyleGAN Encoder
Computing methodologies
Artificial intelligence
Computer vision
Computer graphics
Image manipulation
Index terms have been assigned to the content through auto-classification.
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Published In
Computer Vision – ACCV 2022: 16th Asian Conference on Computer Vision, Macao, China, December 4–8, 2022, Proceedings, Part IV
Dec 2022
780 pages
ISBN:978-3-031-26315-6
DOI:10.1007/978-3-031-26316-3
- Editors:
- Lei Wang
University of Wollongong, Wollongong, NSW, Australia
, - Juergen Gall
University of Bonn, Bonn, Germany
, - Tat-Jun Chin
University of Adelaide, Adelaide, SA, Australia
, - Imari Sato
National Institute of Informatics, Tokyo, Japan
, - Rama Chellappa
Johns Hopkins University, Baltimore, MD, USA
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 02 March 2023
Author Tags
- Kinship face synthesis
- StyleGAN Encoder
Qualifiers
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