# SSAT: A Symmetric Semantic-Aware Transformer Network for Makeup Transfer and Removal

arXiv: https://arxiv.org/abs/2112.03631 · HTML: https://mlsanity.com/paper/2112.03631/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 31 references that are in the map.
- First posted on arXiv 7 Dec 2021.

## Builds on (most cited first, 19 of 19)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [Diverse Image-to-Image Translation via Disentangled Representations](https://mlsanity.com/paper/1808.00948/) (2018)
- [Visual Attribute Transfer through Deep Image Analogy](https://mlsanity.com/paper/1705.01088/) (2017)
- [Cross-domain Correspondence Learning for Exemplar-based Image Translation](https://mlsanity.com/paper/2004.05571/) (2020)
- [Deep Exemplar-based Colorization](https://mlsanity.com/paper/1807.06587/) (2018)
- [PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer](https://mlsanity.com/paper/1909.06956/) (2019)
- [LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup](https://mlsanity.com/paper/1904.11272/) (2019)
- [Reference-Based Sketch Image Colorization using Augmented-Self Reference and Dense Semantic Correspondence](https://mlsanity.com/paper/2005.05207/) (2020)
- [Lipstick ain't enough: Beyond Color Matching for In-the-Wild Makeup Transfer](https://mlsanity.com/paper/2104.01867/) (2021)
- [SOGAN: 3D-Aware Shadow and Occlusion Robust GAN for Makeup Transfer](https://mlsanity.com/paper/2104.10567/) (2021)
- [Facial Attribute Transformers for Precise and Robust Makeup Transfer](https://mlsanity.com/paper/2104.02894/) (2021)
- [PSGAN++: Robust Detail-Preserving Makeup Transfer and Removal](https://mlsanity.com/paper/2105.12324/) (2021)
- [Content and Colour Distillation for Learning Image Translations with the Spatial Profile Loss](https://mlsanity.com/paper/1908.00274/) (2019)
- [Local Facial Makeup Transfer via Disentangled Representation](https://mlsanity.com/paper/2003.12065/) (2020)

## Built on by (most cited first, 22 of 22)

- [Deepfake Generation and Detection: A Benchmark and Survey](https://mlsanity.com/paper/2403.17881/) (2024)
- [Recent Advances in Vision Transformer: A Survey and Outlook of Recent Work](https://mlsanity.com/paper/2203.01536/) (2022)
- [BeautyREC: Robust, Efficient, and Content-preserving Makeup Transfer](https://mlsanity.com/paper/2212.05855/) (2022)
- [Content-Style Decoupling for Unsupervised Makeup Transfer without Generating Pseudo Ground Truth](https://mlsanity.com/paper/2405.17240/) (2024)
- [Transformer-based Generative Adversarial Networks in Computer Vision: A Comprehensive Survey](https://mlsanity.com/paper/2302.08641/) (2023)
- [SHMT: Self-supervised Hierarchical Makeup Transfer via Latent Diffusion Models](https://mlsanity.com/paper/2412.11058/) (2024)
- [MAD: Makeup All-in-One with Cross-Domain Diffusion Model](https://mlsanity.com/paper/2504.02545/) (2025)
- [FLUX-Makeup: High-Fidelity, Identity-Consistent, and Robust Makeup Transfer via Diffusion Transformer](https://mlsanity.com/paper/2508.05069/) (2025)
- [BeautyBank: Encoding Facial Makeup in Latent Space](https://mlsanity.com/paper/2411.11231/) (2024)
- [MoFRR: Mixture of Diffusion Models for Face Retouching Restoration](https://mlsanity.com/paper/2507.19770/) (2025)
- [EvoMakeup: High-Fidelity and Controllable Makeup Editing with MakeupQuad](https://mlsanity.com/paper/2508.05994/) (2025)
- [SARA: Controllable Makeup Transfer with Spatial Alignment and Region-Adaptive Normalization](https://mlsanity.com/paper/2311.16828/) (2023)
- [CSHNet: A Novel Information Asymmetric Image Translation Method](https://mlsanity.com/paper/2501.10197/) (2025)
- [FFHQ-Makeup: Paired Synthetic Makeup Dataset with Facial Consistency Across Multiple Styles](https://mlsanity.com/paper/2508.03241/) (2025)
- [Gorgeous: Create Your Desired Character Facial Makeup from Any Ideas](https://mlsanity.com/paper/2404.13944/) (2024)
- [Supervised makeup transfer with a curated dataset: Decoupling identity and makeup features for enhanced transformation](https://mlsanity.com/paper/2602.00729/) (2026)
- [Retrieval Augmented Image Harmonization](https://mlsanity.com/paper/2412.13916/) (2024)
- [Scalable and Realistic Virtual Try-on Application for Foundation Makeup with Kubelka-Munk Theory](https://mlsanity.com/paper/2507.07333/) (2025)
- [Towards High-Fidelity, Identity-Preserving Real-Time Makeup Transfer: Decoupling Style Generation](https://mlsanity.com/paper/2509.02445/) (2025)
- [Diffusion-Based Makeup Transfer with Facial Region-Aware Makeup Features](https://mlsanity.com/paper/2603.20012/) (2026)
- [From Synthetic to Real: Toward Identity-Consistent Makeup Transfer with Synthetic and Real Data](https://mlsanity.com/paper/2605.07861/) (2026)
- [MagicMakeup: A Region-Controllable Diffusion Transformer for High-Fidelity Makeup-Transfer](https://mlsanity.com/paper/2607.20924/) (2026)

## Abstract (arXiv)

Makeup transfer is not only to extract the makeup style of the reference image, but also to render the makeup style to the semantic corresponding position of the target image. However, most existing methods focus on the former and ignore the latter, resulting in a failure to achieve desired results. To solve the above problems, we propose a unified Symmetric Semantic-Aware Transformer (SSAT) network, which incorporates semantic correspondence learning to realize makeup transfer and removal simultaneously. In SSAT, a novel Symmetric Semantic Corresponding Feature Transfer (SSCFT) module and a weakly supervised semantic loss are proposed to model and facilitate the establishment of accurate semantic correspondence. In the generation process, the extracted makeup features are spatially distorted by SSCFT to achieve semantic alignment with the target image, then the distorted makeup features are combined with unmodified makeup irrelevant features to produce the final result. Experiments show that our method obtains more visually accurate makeup transfer results, and user study in comparison with other state-of-the-art makeup transfer methods reflects the superiority of our method. Besides, we verify the robustness of the proposed method in the difference of expression and pose, object occlusion scenes, and extend it to video makeup transfer. Code will be available at https://gitee.com/sunzhaoyang0304/ssat-msp.