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Beauty is in the eye of the beholder. This maxim, emphasizing thesubjectivity of the perception of beauty, has enjoyed a wide consensus sinceancient times. In the digitalera, data-driven methods have been shown to beable to predict human-assigned beauty scores for facial images. In this work,we augment this ability and train a generative model that generates facesconditioned on a requested beauty score. In addition, we show how this trainedgenerator can be used to beautify an input face image. By doing so, we achievean unsupervised beautification model, in the sense that it relies on no groundtruth target images.

Beholder-GAN: Generation and Beautification of Facial Images with Conditioning on Their Beauty Level