Developing an Avatar Generator and Secure Storage using AI

Arnav Atkare*, Om Akre**, Tanmay Tembhurne***, Sanket Bhanuse****, Om Bhosle*****, Aniket Bhoyar******
*-****** Department of Computer Science and Engineering, S. B. Jain Institute of Technology, Management and Research, Nagpur, India.
Periodicity:July - December'2025

Abstract

Integration of artificial intelligence (AI) in digital identity creation has also introduced sophisticated avatar creation and safe storage. This paper presents a new AI-based system for creating personalized avatars with safe storage. The system, in the proposed scenario, utilizes deep learning methods like Generative Adversarial Networks (GANs) and Neural Style Transfer (NST) to create high-quality avatars from user data in terms of face information and body pose based on pose estimation pipelines like OpenPose and MediaPipe. This paper illuminates the uses of AI-empowered avatar generation in gaming, virtual reality, augmented reality, and online secure identity. Utilizing deep learning, encryption, and blockchain technology together, the suggested system provides naturalistic avatar generation and advanced data protection, privacy, and user control. Experimental results indicate the efficacy and scalability of the system with a suggestion for use in mass usage by multiple industries. Future research can then be directed towards enhancing avatar realism, computationally optimizing, and further developing the support for real-time rendering of avatars from dynamic scenes.

Keywords

AI, Avatar Generation, Deep Learning, Pose Estimation, OpenPose, MediaPipe.

How to Cite this Article?

Atkare, A., Akre, O., Tembhurne, T., Bhanuse, S., Bhosle, O., and Bhoyar, A. (2025). Developing an Avatar Generator and Secure Storage using AI. i-manager’s Journal on Digital Forensics & Cyber Security, 3(2), 10-20.

References

[4]. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901.
[6]. Garodi, H., More, K., Jagtap, N., Chavhan, S., & Vaidya, C. (2015). Performance enhancing in real time operating system by using HYBRID algorithm. International Journal of Computer Science and Mobile Computing, 4(3) 230 – 241.
[10]. Mayer, R. E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology, 93(1), 187–198.
[11]. Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. In International Conference on Machine Learning (pp. 8748-8763). PmLR.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 40 40 300
Online 15 15 300
Pdf & Online 40 40 300

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.