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@article{176153,
        author = {Deepti Dwivedi and Dev Chandani and Anisha Rani and Indra Kishor},
        title = {The Integration of Machine Learning-Driven Avatars in Virtual Reality: Enhancing Realism and User Experience},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {5495-5502},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176153},
        abstract = {With Virtual Reality now made to blend with Machine Learning technology, digital interactions have advanced considerably, allowing dynamic and highly responsive virtual avatars. ML-driven avatars, using motion capture data or sophisticated algorithms and models such as neural networks and behavioral synthesis models, essentially mimic realistic human movements and adjust to real-time user interactions. The work here explores the transformative potential of these avatars into immersive and interactive realists, personalists, and user-engagers in virtual reality environments. The study shows that, by using supervised learning for the predictive motion, and reinforcement learning for adaptive behaviors, there is a significant increase in the immersion of the user and the efficiency of the tasks performed with the ML avatars compared to traditional static models, up to 30% in interactive tasks. These results demonstrate ML avatars' applicability in domains like gaming, education, and even telemedicine, opening ways to more immersive, intelligent VR applications. Challenges such as computational scalability notwithstanding, the integration of ML with VR is one of the key steps toward next-generation virtual systems where realism and interactivity coexist unencumberingly.},
        keywords = {Virtual Reality and Machine Learning Integration, ML-Driven Virtual Avatars, Immersive and Interactive VR Applications, Real-Time Adaptive Avatars in VR},
        month = {April},
        }