Relevant publications

Nir Diamant

N. Diamant, D. Zadok, C. Baskin, E. Schwartz, A. M. Bronstein, Beholder-GAN: Generation and beautification of facial images with conditioning on their beauty level, Proc. Int'l Conf. on Image Processing (ICIP), 2019 details

Beholder-GAN: Generation and beautification of facial images with conditioning on their beauty level

N. Diamant, D. Zadok, C. Baskin, E. Schwartz, A. M. Bronstein
Proc. Int'l Conf. on Image Processing (ICIP), 2019
Picture for Beholder-GAN: Generation and beautification of facial images with conditioning on their beauty level

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.