Beholder-GAN: Generation and Beautification of Facial Images with Conditioning on Their Beauty Level
This addresses the challenge of subjective beauty perception in digital media for applications like photo editing, but it is incremental as it builds on existing beauty prediction methods.
The paper tackles the problem of generating and beautifying facial images by conditioning on beauty scores, achieving an unsupervised beautification model without ground truth targets.
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 digitalera, 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.