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

arXiv: https://arxiv.org/abs/1902.02593 · HTML: https://mlsanity.com/paper/1902.02593/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2019.

## Built on by (most cited first, 6 of 6)

- [Evaluating Text-to-Image Generative Models: An Empirical Study on Human Image Synthesis](https://mlsanity.com/paper/2403.05125/) (2024)
- [Diffusion-based Facial Aesthetics Enhancement with 3D Structure Guidance](https://mlsanity.com/paper/2503.14402/) (2025)
- [Attribute Controllable Beautiful Caucasian Face Generation by Aesthetics Driven Reinforcement Learning](https://mlsanity.com/paper/2208.04517/) (2022)
- [Face Morphing: Fooling a Face Recognition System Is Simple!](https://mlsanity.com/paper/2205.13796/) (2022)
- [Subjective Face Transform using Human First Impressions](https://mlsanity.com/paper/2309.15381/) (2023)
- [Detection of Digital Facial Retouching utilizing Face Beauty Information](https://mlsanity.com/paper/2512.08397/) (2025)

## Abstract (arXiv)

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.