用物理模型加速粉底虚拟试妆，真实感强且可扩展
Scalable and Realistic Virtual Try-on Application for Foundation Makeup with Kubelka-Munk Theory
- 基于库贝尔卡-蒙克理论优化图像合成速度
- 仅靠电商产品信息实现跨品类真实上妆
- 适合美妆科技与增强现实开发者参考
增强现实正在重塑美妆产业，虚拟试妆（VTO）应用让用户无需实际涂抹即可在手机上体验多种粉底。基础挑战在于如何在保持方法可扩展性的同时，精准模拟粉底与肤色的色彩融合效果。本文提出一种新方法，通过近似成熟的库贝尔卡-蒙克（Kubelka-Munk, KM）理论，实现更快的图像合成，同时保留真实的粉底-肤色融合效果。此外，我们构建了一个端到端的可扩展框架，仅依赖电商平台提供的产品信息，即可实现真实感十足的基础妆虚拟试妆。通过真实美妆图像验证，该框架优于现有技术。
原文摘要 · Abstract (English)
Augmented reality is revolutionizing beauty industry with virtual try-on (VTO) applications, which empowers users to try a wide variety of products using their phones without the hassle of physically putting on real products. A critical technical challenge in foundation VTO applications is the accurate synthesis of foundation-skin tone color blending while maintaining the scalability of the method across diverse product ranges. In this work, we propose a novel method to approximate well-established Kubelka-Munk (KM) theory for faster image synthesis while preserving foundation-skin tone color blending realism. Additionally, we build a scalable end-to-end framework for realistic foundation makeup VTO solely depending on the product information available on e-commerce sites. We validate our method using real-world makeup images, demonstrating that our framework outperforms other techniques.
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