Year: 2025
Type: article
Abstract: Deep learning advancements have made tremendous progress in AI-based avatar creation.This research presents a holistic system that integrates generative adversarial networks (GANs) and transformer-style architectures to generate high-fidelity, customized avatars from sparse input data.The system proposed here offers an avatar video generator with lipsync functionality to produce realistic digital human likenesses with synchronized voice.The system utilizes several state-of-the-art deep learning models, such as Wav2Lip and its variants, to provide robust lip-sync between audio inputs and avatar movement.Our solution provides flexible input methods, including voice commands, text, audio file uploads, and video dubbing.We have created an easy-to-use interface with several avatar choices for individualized content generation.In addition, the system includes functionality for social media content creation using integration with big language models such as Gemini 1.5-flash.The authentication mechanism employs JWT-based login via Google accounts, with users' data maintained in MongoDB Atlas.Our testing proves the system's competence across different use cases such as multilingual content generation, educational use, and social media interactions.This work addresses implementation specifics, system structure, evaluation findings, and perspectives for further development of digital human generation.
Authors: Kartarsingh Sajjansingh Gothwal, Krushna Gore, Sejal Hage, Tanvi Gunjal
Institution: Vishwakarma Institute of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7113901972
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