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@article{204680,
        author = {Pooja Mahendra Borade and Priyanka Namdev Sandhan and Hemangi Jaywant Deokar and Pratiksha Sharad Badhan},
        title = {Virtual Try On System For Women’s Clothing},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {3888-3895},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204680},
        abstract = {Online fashion platforms have grown quickly, but many customers still feel unsure when buying clothes because they cannot try them on beforehand. This challenge is even more noticeable with women’s ethnic wear, where proper fitting, draping, and overall look play a crucial role in the final appearance. This paper introduces a virtual try-on system that enables users to see how different garments would look on their own images. The system is built using an image processing pipeline and a modular backend developed with FastAPI. It also includes a category-aware approach, allowing it to handle different types of clothing, such as kurtis and lehengas, in a more effective and tailored manner. To make the results look more natural, a post-processing step is used to preserve the user’s facial features and body structure. The system is designed to run efficiently without the need for high-end hardware. Experimental results indicate that it can generate visually realistic outputs with proper garment alignment. Overall, the system proves to be practical, scalable, and suitable for real-world use in online fashion platforms.},
        keywords = {Virtual Try-On, Diffusion Models, Computer Vision, FastAPI, Image Processing, Deep Learning, Ethnic Wear, Realistic Visualizations, Garment Fit, E-commerce},
        month = {June},
        }