Scalable and Realistic Virtual Try-on Application for Foundation Makeup with Kubelka-Munk Theory
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908443079606272 |
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| author | Pang, Hui Hadap, Sunil Shevchenko, Violetta Suresh, Rahul Banitalebi-Dehkordi, Amin |
| author_facet | Pang, Hui Hadap, Sunil Shevchenko, Violetta Suresh, Rahul Banitalebi-Dehkordi, Amin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07333 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scalable and Realistic Virtual Try-on Application for Foundation Makeup with Kubelka-Munk Theory Pang, Hui Hadap, Sunil Shevchenko, Violetta Suresh, Rahul Banitalebi-Dehkordi, Amin Computer Vision and Pattern Recognition I.4.9 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. |
| title | Scalable and Realistic Virtual Try-on Application for Foundation Makeup with Kubelka-Munk Theory |
| topic | Computer Vision and Pattern Recognition I.4.9 |
| url | https://arxiv.org/abs/2507.07333 |