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

## TL;DR

This paper introduces Beholder-GAN, a generative model that creates and beautifies facial images based on specified beauty levels, enabling subjective beauty manipulation without requiring paired training data.

## Contribution

It presents a novel GAN-based framework for generating and beautifying faces conditioned on beauty scores, without needing ground truth target images.

## Key findings

- Successfully generates faces with controllable beauty levels.
- Can beautify input images without supervised target data.
- Demonstrates subjective beauty manipulation in facial images.

## 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 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.