# BeautyBank: Encoding Facial Makeup in Latent Space

arXiv: https://arxiv.org/abs/2411.11231 · HTML: https://mlsanity.com/paper/2411.11231/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 63 references that are in the map.
- First posted on arXiv 18 Nov 2024.

## Builds on (most cited first, 30 of 48)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://mlsanity.com/paper/2211.09794/) (2022)
- [Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation](https://mlsanity.com/paper/2008.00951/) (2020)
- [Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?](https://mlsanity.com/paper/1904.03189/) (2019)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [Designing an Encoder for StyleGAN Image Manipulation](https://mlsanity.com/paper/2102.02766/) (2021)
- [Diffusion Autoencoders: Toward a Meaningful and Decodable Representation](https://mlsanity.com/paper/2111.15640/) (2021)
- [Pivotal Tuning for Latent-based Editing of Real Images](https://mlsanity.com/paper/2106.05744/) (2021)
- [GAN Inversion: A Survey](https://mlsanity.com/paper/2101.05278/) (2021)
- [ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement](https://mlsanity.com/paper/2104.02699/) (2021)
- [HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing](https://mlsanity.com/paper/2111.15666/) (2021)
- [High-Fidelity GAN Inversion for Image Attribute Editing](https://mlsanity.com/paper/2109.06590/) (2021)
- [StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators](https://mlsanity.com/paper/2108.00946/) (2021)
- [The Contextual Loss for Image Transformation with Non-Aligned Data](https://mlsanity.com/paper/1803.02077/) (2018)
- [Resolution Dependent GAN Interpolation for Controllable Image Synthesis Between Domains](https://mlsanity.com/paper/2010.05334/) (2020)
- [HyperInverter: Improving StyleGAN Inversion via Hypernetwork](https://mlsanity.com/paper/2112.00719/) (2021)
- [Protecting Facial Privacy: Generating Adversarial Identity Masks via Style-robust Makeup Transfer](https://mlsanity.com/paper/2203.03121/) (2022)
- [A Latent Transformer for Disentangled Face Editing in Images and Videos](https://mlsanity.com/paper/2106.11895/) (2021)
- [PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer](https://mlsanity.com/paper/1909.06956/) (2019)
- [Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer](https://mlsanity.com/paper/2203.13248/) (2022)
- [BlendFace: Re-designing Identity Encoders for Face-Swapping](https://mlsanity.com/paper/2307.10854/) (2023)
- [LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup](https://mlsanity.com/paper/1904.11272/) (2019)
- [ClipFace: Text-guided Editing of Textured 3D Morphable Models](https://mlsanity.com/paper/2212.01406/) (2022)
- [LEDITS: Real Image Editing with DDPM Inversion and Semantic Guidance](https://mlsanity.com/paper/2307.00522/) (2023)
- [Stable-Makeup: When Real-World Makeup Transfer Meets Diffusion Model](https://mlsanity.com/paper/2403.07764/) (2024)
- [CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Search](https://mlsanity.com/paper/2306.10008/) (2023)

## Built on by (most cited first, 4 of 4)

- [FFHQ-Makeup: Paired Synthetic Makeup Dataset with Facial Consistency Across Multiple Styles](https://mlsanity.com/paper/2508.03241/) (2025)
- [Supervised makeup transfer with a curated dataset: Decoupling identity and makeup features for enhanced transformation](https://mlsanity.com/paper/2602.00729/) (2026)
- [Diffusion-Based Makeup Transfer with Facial Region-Aware Makeup Features](https://mlsanity.com/paper/2603.20012/) (2026)
- [MagicMakeup: A Region-Controllable Diffusion Transformer for High-Fidelity Makeup-Transfer](https://mlsanity.com/paper/2607.20924/) (2026)

## Abstract (arXiv)

The advancement of makeup transfer, editing, and image encoding has demonstrated their effectiveness and superior quality. However, existing makeup works primarily focus on low-dimensional features such as color distributions and patterns, limiting their versatillity across a wide range of makeup applications. Futhermore, existing high-dimensional latent encoding methods mainly target global features such as structure and style, and are less effective for tasks that require detailed attention to local color and pattern features of makeup. To overcome these limitations, we propose BeautyBank, a novel makeup encoder that disentangles pattern features of bare and makeup faces. Our method encodes makeup features into a high-dimensional space, preserving essential details necessary for makeup reconstruction and broadening the scope of potential makeup research applications. We also propose a Progressive Makeup Tuning (PMT) strategy, specifically designed to enhance the preservation of detailed makeup features while preventing the inclusion of irrelevant attributes. We further explore novel makeup applications, including facial image generation with makeup injection and makeup similarity measure. Extensive empirical experiments validate that our method offers superior task adaptability and holds significant potential for widespread application in various makeup-related fields. Furthermore, to address the lack of large-scale, high-quality paired makeup datasets in the field, we constructed the Bare-Makeup Synthesis Dataset (BMS), comprising 324,000 pairs of 512x512 pixel images of bare and makeup-enhanced faces.