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@article{201169,
        author = {Anurag Surve and Dr. Vinodpuri Gosavi and Sanjyot Patil and Tejal Hande and Anish Uttekar},
        title = {AI FITTING ROOM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {2956-2961},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201169},
        abstract = {A persistent challenge in digital fashion retail is the inability of shoppers to accurately assess how garments will look and fit prior to making a purchase. This limitation contributes to diminished buyer confidence, elevated return rates, and inefficient product discovery processes. To address these issues, this paper introduces an AI-driven virtual fitting room platform that integrates computer vision, machine learning, and augmented reality to facilitate realistic virtual garment try-on and deliver context-aware fashion recommendations. The system architecture incorporates Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and semantic segmentation models to achieve high-fidelity visualization and personalized outfit suggestions tailored to individual body morphology, aesthetic preferences, and prevailing style trends. Experimental validation yields an SSIM score of 0.924, an IoU score of 0.943, and a user satisfaction rating of 4.31 out of 5, reflecting marked improvements over comparable state-of-the-art solutions. Deployment outcomes further indicate a prospective 30% reduction in merchandise return rates and a potential 200% uplift in customer conversion rates.},
        keywords = {Virtual Try-On, Computer Vision, Deep Learning, Fashion Technology, Augmented Reality, Recommendation Systems},
        month = {May},
        }