Title: Regression Guided Strategy to Automated Facial Beauty Optimization through Image Synthesis Show Chinese title

Title: 基于回归的策略通过图像合成实现自动面部美丽优化

Abstract: The use of beauty filters on social media, which enhance the appearance of individuals in images, is a well-researched area, with existing methods proving to be highly effective. Traditionally, such enhancements are performed using rule-based approaches that leverage domain knowledge of facial features associated with attractiveness, applying very specific transformations to maximize these attributes. In this work, we present an alternative approach that projects facial images as points on the latent space of a pre-trained GAN, which are then optimized to produce beautiful faces. The movement of the latent points is guided by a newly developed facial beauty evaluation regression network, which learns to distinguish attractive facial features, outperforming many existing facial beauty evaluation models in this domain. By using this data-driven approach, our method can automatically capture holistic patterns in beauty directly from data rather than relying on predefined rules, enabling more dynamic and potentially broader applications of facial beauty editing. This work demonstrates a potential new direction for automated aesthetic enhancement, offering a complementary alternative to existing methods. Show Chinese abstract

Abstract: 在社交媒体上使用美容滤镜来增强图像中个体的外貌，是一个被广泛研究的领域，现有方法已被证明非常有效。 传统上，这种增强是通过基于规则的方法实现的，这些方法利用与吸引力相关的面部特征的领域知识，对图像应用非常具体的变换以最大化这些特征。 在本工作中，我们提出了一种替代方法，将面部图像投影到预训练GAN的潜在空间中，然后对其进行优化以生成美丽的面部。 潜在点的移动由一个新开发的面部美丽评估回归网络引导，该网络学习区分有吸引力的面部特征，在这一领域优于许多现有的面部美丽评估模型。 通过使用这种数据驱动的方法，我们的方法可以直接从数据中自动捕捉美的整体模式，而不是依赖于预定义的规则，从而实现了更动态且可能更广泛的应用。 这项工作展示了一个自动化美学增强的新方向，为现有方法提供了一种互补的替代方案。