用GAN自动生成适配人脸的美妆风格，让数字化妆更智能。
Protégé: Learn and Generate Basic Makeup Styles with Generative Adversarial Networks (GANs)
- 基于GAN学习人脸与妆容关系，自动设计匹配的妆容。
- 可生成多样妆容，避免传统方法依赖人工设计。
- 适合想快速尝试新妆容的用户或美妆开发者。
化妆已从实体应用转向数字方式，人们通过手机应用为照片添加虚拟妆容并分享至社交平台。然而，尽管这一转变提升了可及性，针对不同人脸设计多样化的个性化妆容仍需人工完成。现有系统如妆容推荐引擎和妆容迁移技术，在为不同个体直观生成创新妆容方面存在局限：需大量用户参与和专业知识，且可用妆容种类有限。本文提出Protégé，一种新型数字化妆应用，利用先进的生成对抗网络（GANs）学习并自动创建适配人脸的妆容风格。该任务是现有化妆应用（如基于专家系统的推荐系统和妆容迁移方法）无法实现的。通过大量实验验证，Protégé展现了在学习与生成多样化妆容方面的强大能力，提供了一种便捷、直观的解决方案，标志着数字化妆技术的重大突破。
原文摘要 · Abstract (English)
Makeup is no longer confined to physical application; people now use mobile apps to digitally apply makeup to their photos, which they then share on social media. However, while this shift has made makeup more accessible, designing diverse makeup styles tailored to individual faces remains a challenge. This challenge currently must still be done manually by humans. Existing systems, such as makeup recommendation engines and makeup transfer techniques, offer limitations in creating innovative makeups for different individuals "intuitively" -- significant user effort and knowledge needed and limited makeup options available in app. Our motivation is to address this challenge by proposing Protégé, a new makeup application, leveraging recent generative model -- GANs to learn and automatically generate makeup styles. This is a task that existing makeup applications (i.e., makeup recommendation systems using expert system and makeup transfer methods) are unable to perform. Extensive experiments has been conducted to demonstrate the capability of Protégé in learning and creating diverse makeups, providing a convenient and intuitive way, marking a significant leap in digital makeup technology!
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