AAAI 2023technical1 citations
Invertible Conditional GAN Revisited: Photo-to-Manga Face Translation with Modern Architectures (Student Abstract)
Taro Hatakeyama, Ryusuke Saito, Komei Hiruta, Atsushi Hashimoto, Satoshi Kurihara
Abstract
Recent style translation methods have extended their transferability from texture to geometry. However, performing translation while preserving image content when there is a significant style difference is still an open problem. To overcome this problem, we propose Invertible Conditional Fast GAN (IcFGAN) based on GAN inversion and cFGAN. It allows for unpaired photo-to-manga face translation. Experimental results show that our method could translate styles under significant style gaps, while the state-of-the-art methods could hardly preserve image content.
BibTeX
@article{Hatakeyama_Saito_Hiruta_Hashimoto_Kurihara_2024, title={Invertible Conditional GAN Revisited: Photo-to-Manga Face Translation with Modern Architectures (Student Abstract)}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26972}, DOI={10.1609/aaai.v37i13.26972}, abstractNote={Recent style translation methods have extended their transferability from texture to geometry. However, performing translation while preserving image content when there is a significant style difference is still an open problem. To overcome this problem, we propose Invertible Conditional Fast GAN (IcFGAN) based on GAN inversion and cFGAN. It allows for unpaired photo-to-manga face translation. Experimental results show that our method could translate styles under significant style gaps, while the state-of-the-art methods could hardly preserve image content.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Hatakeyama, Taro and Saito, Ryusuke and Hiruta, Komei and Hashimoto, Atsushi and Kurihara, Satoshi}, year={2024}, month={Jul.}, pages={16224-16225} }