Mobile Fitting Room: On-device Virtual Try-on via Diffusion Models
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914664616558592 |
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| author | Blalock, Justin Munechika, David Karanth, Harsha Helbling, Alec Mehta, Pratham Lee, Seongmin Chau, Duen Horng |
| author_facet | Blalock, Justin Munechika, David Karanth, Harsha Helbling, Alec Mehta, Pratham Lee, Seongmin Chau, Duen Horng |
| contents | The growing digital landscape of fashion e-commerce calls for interactive and user-friendly interfaces for virtually trying on clothes. Traditional try-on methods grapple with challenges in adapting to diverse backgrounds, poses, and subjects. While newer methods, utilizing the recent advances of diffusion models, have achieved higher-quality image generation, the human-centered dimensions of mobile interface delivery and privacy concerns remain largely unexplored. We present Mobile Fitting Room, the first on-device diffusion-based virtual try-on system. To address multiple inter-related technical challenges such as high-quality garment placement and model compression for mobile devices, we present a novel technical pipeline and an interface design that enables privacy preservation and user customization. A usage scenario highlights how our tool can provide a seamless, interactive virtual try-on experience for customers and provide a valuable service for fashion e-commerce businesses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01877 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mobile Fitting Room: On-device Virtual Try-on via Diffusion Models Blalock, Justin Munechika, David Karanth, Harsha Helbling, Alec Mehta, Pratham Lee, Seongmin Chau, Duen Horng Human-Computer Interaction Artificial Intelligence Machine Learning The growing digital landscape of fashion e-commerce calls for interactive and user-friendly interfaces for virtually trying on clothes. Traditional try-on methods grapple with challenges in adapting to diverse backgrounds, poses, and subjects. While newer methods, utilizing the recent advances of diffusion models, have achieved higher-quality image generation, the human-centered dimensions of mobile interface delivery and privacy concerns remain largely unexplored. We present Mobile Fitting Room, the first on-device diffusion-based virtual try-on system. To address multiple inter-related technical challenges such as high-quality garment placement and model compression for mobile devices, we present a novel technical pipeline and an interface design that enables privacy preservation and user customization. A usage scenario highlights how our tool can provide a seamless, interactive virtual try-on experience for customers and provide a valuable service for fashion e-commerce businesses. |
| title | Mobile Fitting Room: On-device Virtual Try-on via Diffusion Models |
| topic | Human-Computer Interaction Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2402.01877 |