Abstract
This paper applies a meta-verse strategy as an intelligent application works as a virtual reality for the dressing room. The proposed website is designed as a virtual fashion store, such that the women and/or men may foumd their fashion with a privacy and convenience characteristics based on the E-commerce and meta-verse strategy. The proposed website is a real-time virtual dressing room that eliminates a lot of the trouble from shop** by which no more long time required in the fitting room with an armful of clothing or the time-consuming process of getting dressed and undressed multiple times. Stand a few feet in front of a webcam to utilize this device, which uses motion-sensing technology. On the computer screen, a live image of you will display, along with various categories such as trousers, shirts, and dresses. By waving your hand over Translation Controls, Scale Controls, and Selection Controls, you may select a category, such as shirts and the preferred colors. Therefore, the clothing you wish to try on will be on, and it will be digitally overlaid over your live image. A quick size algorithm to assist the user in determining the prices and appropriate size is proposed. Moreover, we present RSA for user authentication to make the system more secure especially for mobility clients. As a result, the primary rule is that the proposed meta-verse strategy should generate pictures in real-time and respond to user interaction in real-time. It should also be a low-cost, user-friendly system.
Mahmoud Y. Shams: Scientific Research Group in Egypt
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Acknowledgements
We want to thank Misr Higher Institute for Commerce and Computers, especially Eng. Essam Nabil and Ahmed Bedawy, Amora Mahmoud, Basem Ghanem, Noura Al Hosainy, and Omar Fathy for their assistant in website implementation.
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Shams, M.Y., Elzeki, O.M., Marie, H.S. (2023). Towards 3D Virtual Dressing Room Based User-Friendly Metaverse Strategy. In: Hassanien, A.E., Darwish, A., Torky, M. (eds) The Future of Metaverse in the Virtual Era and Physical World. Studies in Big Data, vol 123. Springer, Cham. https://doi.org/10.1007/978-3-031-29132-6_2
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