StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation

Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and robustness in handling more than two domains, since different models should be built independently for every pair of image domains. To address this li...

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Veröffentlicht in:2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition S. 8789 - 8797
Hauptverfasser: Choi, Yunjey, Choi, Minje, Kim, Munyoung, Ha, Jung-Woo, Kim, Sunghun, Choo, Jaegul
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2018
Schlagworte:
Gallium nitride
Generative adversarial networks
Generators
Hair
Image reconstruction
Task analysis
Training
ISSN:1063-6919
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Zusammenfassung:Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and robustness in handling more than two domains, since different models should be built independently for every pair of image domains. To address this limitation, we propose StarGAN, a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model. Such a unified model architecture of StarGAN allows simultaneous training of multiple datasets with different domains within a single network. This leads to StarGAN's superior quality of translated images compared to existing models as well as the novel capability of flexibly translating an input image to any desired target domain. We empirically demonstrate the effectiveness of our approach on a facial attribute transfer and a facial expression synthesis tasks.
ISSN:1063-6919
DOI:10.1109/CVPR.2018.00916