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In the fashion industry, there is a growing demand for virtual try-on experiences that allow customers to see how clothes and accessories would look on them before making a purchase.
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However, traditional virtual try-on systems often rely on static data and fail to account for the dynamic nature of fashion trends and individual preferences. This can result in inaccurate and unappealing virtual try-on experiences that do not meet the needs of customers.
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To address this challenge, there is a need for an AI-powered fashion try-on system that can incorporate dynamic data to provide more accurate and personalized virtual try-on experiences. This system should be able to analyze real-time data on fashion trends and individual preferences to generate virtual try-on experiences that are tailored to the needs of each customer. By leveraging the power of AI and dynamic data, this system has the potential to revolutionize the way customers shop for clothes and accessories.
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There are various more Future Works, including increaseing the body detection pivot points, to get better accuracy, as we can observe in the below output image, few points are being missed out.
- Changed the max pose limit:
- To get this more accurate result:
The data used for training our AI model was taken from Dress Code Dataset © Yoox Net-a-Porter Group S.p.A., with Signed Agreeement of not sharing it anywhere.
- Siddharth Shankar Das
- Daksh Bagga
- Moin Ahmed Zahir