Protégé：利用生成对抗网络（GANs）学习并生成基础妆容风格
Protégé: Learn and Generate Basic Makeup Styles with Generative Adversarial Networks (GANs)
AI总结：
本文提出Protégé应用，利用GANs自动学习并生成多样化妆容风格，克服现有推荐与迁移方法需人工设计且选项有限的局限，实现直观便捷的数字化妆。
摘要：
化妆不再局限于实体涂抹；人们现在使用移动应用为照片数字化上妆，并分享到社交媒体。然而，尽管这一转变让化妆更易获得，为不同面容设计多样化的妆容风格仍是一项挑战，目前仍需人工手动完成。现有系统，如妆容推荐引擎和妆容迁移技术，在“直观地”为不同个体创造新颖妆容方面存在局限——需要大量用户操作和专业知识，且应用中可用的妆容选项有限。我们的动机是通过提出Protégé这一新型化妆应用来应对该挑战，利用近期生成模型——GANs来学习并自动生成妆容风格。这是现有化妆应用（即基于专家系统的妆容推荐系统和妆容迁移方法）无法完成的任务。我们开展了大量实验，证明了Protégé在学习和创造多样化妆容方面的能力，提供了一种便捷且直观的方式，标志着数字化妆技术的重大飞跃！
英文摘要：
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!
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
