用高质量数据集和扩散模型实现精准可控的妆容迁移。
Supervised makeup transfer with a curated dataset: Decoupling identity and makeup features for enhanced transformation
- 构建合成+真实+筛选的数据集提升多样性与真实感。
- 分离身份与妆容特征，保留原脸结构与肤色。
- 支持自然语言控制眼妆、唇妆等局部调整，适合美妆应用。
扩散模型在生成任务中表现出色，为妆容迁移提供了比GAN更稳定的替代方案。现有方法常受限于数据集质量差、身份与妆容特征混淆、控制能力弱等问题。本文提出三项改进：首先，采用训练-生成-筛选-再训练策略构建高质量数据集，融合合成、真实与过滤样本以增强多样性与保真度；其次，设计基于扩散模型的框架，实现身份与妆容特征的解耦，确保面部结构与肤色保持不变的同时精准施加多样化妆容风格；第三，提出文本引导机制，支持细粒度、区域化的控制，用户可通过自然语言提示精确调整眼部、唇部或整体妆容。在多个基准和真实场景下的实验表明，该方法在保真度、身份保留和灵活性方面均有显著提升。数据集示例可见：https://makeup-adapter.github.io。
原文摘要 · Abstract (English)
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.
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