# Face Beautification: Beyond Makeup Transfer

arXiv: https://arxiv.org/abs/1912.03630 · HTML: https://mlsanity.com/paper/1912.03630/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2019.

## Built on by (most cited first, 7 of 7)

- [Facial Attribute Transformers for Precise and Robust Makeup Transfer](https://mlsanity.com/paper/2104.02894/) (2021)
- [DeepBlur: A Simple and Effective Method for Natural Image Obfuscation](https://mlsanity.com/paper/2104.02655/) (2021)
- [Toward Tiny and High-quality Facial Makeup with Data Amplify Learning](https://mlsanity.com/paper/2403.15033/) (2024)
- [BeautyBank: Encoding Facial Makeup in Latent Space](https://mlsanity.com/paper/2411.11231/) (2024)
- [Diffusion-based Facial Aesthetics Enhancement with 3D Structure Guidance](https://mlsanity.com/paper/2503.14402/) (2025)
- [Modern Augmented Reality: Applications, Trends, and Future Directions](https://mlsanity.com/paper/2202.09450/) (2022)
- [FFHQ-Makeup: Paired Synthetic Makeup Dataset with Facial Consistency Across Multiple Styles](https://mlsanity.com/paper/2508.03241/) (2025)

## Abstract (arXiv)

Facial appearance plays an important role in our social lives. Subjective perception of women's beauty depends on various face-related (e.g., skin, shape, hair) and environmental (e.g., makeup, lighting, angle) factors. Similar to cosmetic surgery in the physical world, virtual face beautification is an emerging field with many open issues to be addressed. Inspired by the latest advances in style-based synthesis and face beauty prediction, we propose a novel framework of face beautification. For a given reference face with a high beauty score, our GAN-based architecture is capable of translating an inquiry face into a sequence of beautified face images with referenced beauty style and targeted beauty score values. To achieve this objective, we propose to integrate both style-based beauty representation (extracted from the reference face) and beauty score prediction (trained on SCUT-FBP database) into the process of beautification. Unlike makeup transfer, our approach targets at many-to-many (instead of one-to-one) translation where multiple outputs can be defined by either different references or varying beauty scores. Extensive experimental results are reported to demonstrate the effectiveness and flexibility of the proposed face beautification framework.