Scalable and Realistic Virtual Try-on Application for Foundation Makeup with Kubelka-Munk Theory
This work addresses a domain-specific problem for the beauty industry by enabling scalable and realistic virtual try-on of foundation makeup, though it appears incremental as it builds on existing theory.
The paper tackles the challenge of accurately blending foundation makeup with skin tones in virtual try-on applications by proposing a method that approximates Kubelka-Munk theory for faster image synthesis while maintaining realism, and it demonstrates outperformance over other techniques using real-world makeup images.
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.