Enhanced Virtual Try-On in the Metaverse Leveraging Unet Model for Improved Cloth Detection

This paper introduces a Unet-based architecture for enhanced virtual try-on applications within the Metaverse, leveraging the rapid advancements in 6G technology. Our model, built on PyTorch 2.1.2 and tested on an NVIDIA Tesla P100-PCIE-16GB GPU, demonstrates remarkable proficiency in cloth detection, a critical aspect of virtual fitting rooms. We evaluate our model using a Kaggle dataset, achieving a significant accuracy of 96% and a Dice score above 1.65 in our tests, indicating a high degree of precision in garment segmentation. The synergy between our model's deep learning capabilities and the high-speed, low-latency properties of 6G networks promises a revolutionary virtual try-on experience catering to the nuanced demands of digital fashion in the Metaverse ecosystem.