Image-to-Image Translation Method for Game-Character Face Generation
- Authors
- Kang, Shinjin; S.; Ok; Y.; Kim; H.; Hahn; T.
- 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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Collections - School of Games > Game Software Major > 1. Journal Articles
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