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Image-to-Image Translation Method for Game-Character Face Generation

Authors
Kang, ShinjinS.OkY.KimH.HahnT.
Issue Date
2020
Publisher
IEEE Computer Society
Keywords
Game-Character Generation; Generative Adversarial Network; Image-to-image Translation
Citation
IEEE Conference on Computatonal Intelligence and Games, CIG, v.2020-August, pp.628 - 631
Journal Title
IEEE Conference on Computatonal Intelligence and Games, CIG
Volume
2020-August
Start Page
628
End Page
631
URI
https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/12516
DOI
10.1109/CoG47356.2020.9231650
ISSN
2325-4270
Abstract
Traditional image-to-image translation methods effectively change the style; however, these methods have several limitations in shape changing. Particularly, current image-to-image translation technology is not effective for changing a real-world face image to the face of a virtual character. To solve this problem, we propose a novel unsupervised image-to-image translation method that is specialized in facial changes accompanied by radical shape changes. We apply two feature loss functions specialized for faces in an image-to-image translation technique based on the generative adversarial network framework. The experimental results show that the proposed method is superior to other recent image-to-image algorithms in case of face deformations. © 2020 IEEE.
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