GAN Inversion with Editable StyleMap

So Honda, Ryohei Orihara, Yuichi Sei, Yasuyuki Tahara, Akihiko Ohsuga

2023

Abstract

Recently, the field of GAN Inversion, which estimates the latent code of a GAN to reproduce the desired image, has attracted much attention. Once a latent variable that reproduces the input image is obtained, the image can be edited by manipulating the latent code. However, it is known that there is a trade-off between reconstruction quality, which is the difference between the input image and the reproduced image, and editability, which is the plausibility of the edited image. In our study, we attempted to improve reconstruction quality by extending latent code that represents the properties of the entire image in the spatial direction. Next, since such an expansion significantly impairs the editing quality, we performed a GAN Inversion that realizes both reconstruction quality and editability by imposing an additional regularization. As a result, the proposed method yielded a better trade-off between the reconstruction quality and the editability against the baseline from both quantitative and qualitative perspectives, and is comparable to state-of-the-art(SOTA) methods that adjust the weights of the generators.

Paper Citation

in Harvard Style

Honda S., Orihara R., Sei Y., Tahara Y. and Ohsuga A. (2023). GAN Inversion with Editable StyleMap. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 389-396. DOI: 10.5220/0011676400003393

in Bibtex Style

@conference{icaart23,
author={So Honda and Ryohei Orihara and Yuichi Sei and Yasuyuki Tahara and Akihiko Ohsuga},
title={GAN Inversion with Editable StyleMap},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={389-396},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011676400003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - GAN Inversion with Editable StyleMap
SN - 978-989-758-623-1
AU - Honda S.
AU - Orihara R.
AU - Sei Y.
AU - Tahara Y.
AU - Ohsuga A.
PY - 2023
SP - 389
EP - 396
DO - 10.5220/0011676400003393