@inproceedings{356cc70415d44300b2e574a6f851cc80,
title = "Beholder-Gan: Generation and Beautification of Facial Images with Conditioning on Their Beauty Level",
abstract = "'Beauty is in the eye of the beholder.' This maxim, emphasizing the subjectivity of the perception of beauty, has enjoyed a wide consensus since ancient times. In the digital era, data-driven methods have been shown to be able to predict human-assigned beauty scores for facial images. In this work, we augment this ability and train a generative model that generates faces conditioned on a requested beauty score. In addition, we show how this trained generator can be used to 'beautify' an input face image. By doing so, we achieve an unsupervised beautification model, in the sense that it relies on no ground truth target images. Our implementation is available on: https://github.com/beholdergan/Beholder-GAN.",
keywords = "Beautification, CGAN, Face synthesis, GAN, Generative Adversarial Network",
author = "Nir Diamant and Dean Zadok and Chaim Baskin and Eli Schwartz and Bronstein, {Alex M.}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 26th IEEE International Conference on Image Processing, ICIP 2019 ; Conference date: 22-09-2019 Through 25-09-2019",
year = "2019",
month = sep,
day = "1",
doi = "10.1109/ICIP.2019.8803807",
language = "English",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "739--743",
booktitle = "2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings",
address = "United States",
}