Supervised makeup transfer with a curated dataset: Decoupling identity and makeup features for enhanced transformation
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917238849667072 |
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| author | Pan, Qihe Wu, Yiming Zhao, Xing Xie, Liang Sun, Guodao Liang, Ronghua |
| author_facet | Pan, Qihe Wu, Yiming Zhao, Xing Xie, Liang Sun, Guodao Liang, Ronghua |
| contents | Diffusion models have recently shown strong progress in generative tasks, offering a more stable alternative to GAN-based approaches for makeup transfer. Existing methods often suffer from limited datasets, poor disentanglement between identity and makeup features, and weak controllability. To address these issues, we make three contributions. First, we construct a curated high-quality dataset using a train-generate-filter-retrain strategy that combines synthetic, realistic, and filtered samples to improve diversity and fidelity. Second, we design a diffusion-based framework that disentangles identity and makeup features, ensuring facial structure and skin tone are preserved while applying accurate and diverse cosmetic styles. Third, we propose a text-guided mechanism that allows fine-grained and region-specific control, enabling users to modify eyes, lips, or face makeup with natural language prompts. Experiments on benchmarks and real-world scenarios demonstrate improvements in fidelity, identity preservation, and flexibility. Examples of our dataset can be found at: https://makeup-adapter.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_00729 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Supervised makeup transfer with a curated dataset: Decoupling identity and makeup features for enhanced transformation Pan, Qihe Wu, Yiming Zhao, Xing Xie, Liang Sun, Guodao Liang, Ronghua Computer Vision and Pattern Recognition Diffusion models have recently shown strong progress in generative tasks, offering a more stable alternative to GAN-based approaches for makeup transfer. Existing methods often suffer from limited datasets, poor disentanglement between identity and makeup features, and weak controllability. To address these issues, we make three contributions. First, we construct a curated high-quality dataset using a train-generate-filter-retrain strategy that combines synthetic, realistic, and filtered samples to improve diversity and fidelity. Second, we design a diffusion-based framework that disentangles identity and makeup features, ensuring facial structure and skin tone are preserved while applying accurate and diverse cosmetic styles. Third, we propose a text-guided mechanism that allows fine-grained and region-specific control, enabling users to modify eyes, lips, or face makeup with natural language prompts. Experiments on benchmarks and real-world scenarios demonstrate improvements in fidelity, identity preservation, and flexibility. Examples of our dataset can be found at: https://makeup-adapter.github.io. |
| title | Supervised makeup transfer with a curated dataset: Decoupling identity and makeup features for enhanced transformation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.00729 |