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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2021.3061933
|2 doi
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|a pubmed24n1073.xml
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|a DE-627
|b ger
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|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Xu, Weihong
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a RelightGAN
|b Instance-level Generative Adversarial Network for Face Illumination Transfer
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| 264 |
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|c 2021
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Revised 10.03.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Face illumination perception and processing is a significantly difficult issue especially due to asymmetric shadings, local highlights, and local shadows. This study focuses on the face illumination transfer problem, which is to transfer the illumination style from a reference face image to a target face image while preserving other attributes. Such an instance-level transfer task is more challenging than the domain-level one that only considers the pre-defined lighting categories. To tackle this problem, we develop an instance-level conditional Generative Adversarial Networks (GAN). Specifically, face identifier is integrated into GAN learning, which enables an individual-specific low-level visual generation. Moreover, the illumination-inspired attention mechanism is conducted to allow GAN to well handle the local lighting effect. Our method requires neither lighting categorization, 3D information, nor strict face alignment, which are often employed by traditional methods. Experiments demonstrate that our method achieves significantly better results than previous methods
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Xie, Xiaohua
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lai, Jianhuang
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 30(2021) vom: 01., Seite 3450-3460
|w (DE-627)NLM09821456X
|x 1941-0042
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| 773 |
1 |
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|g volume:30
|g year:2021
|g day:01
|g pages:3450-3460
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|u http://dx.doi.org/10.1109/TIP.2021.3061933
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|d 30
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|h 3450-3460
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