US20260065567
2026-03-05
Physics
G06T13/40
A computing system is designed to generate a three-dimensional avatar that mirrors a user's appearance and behavior. This system utilizes video and non-video data from a user device, processed through a generative machine learning model. The avatar is crafted to not only resemble the user visually but also to replicate their voice, behavior, and interaction style. The system isolates the user from their video background and clones their voice, storing the avatar and voice data in a network-accessible location. This allows the avatar to be deployed in third-party applications for real-time interactions.
The system employs advanced machine learning to create hyper-realistic avatars. By preprocessing video data to eliminate backgrounds and focusing on the user, it generates a detailed digital likeness. Voice cloning involves extracting audio and synthesizing a voice that matches the user's tone, pace, and accent. The avatars can interact in real-time, with responses generated by a large language model and synchronized lip movements, enhancing the realism of the interaction.
Current avatar technologies are limited by their reliance on pre-generated scripts and high-end processing requirements. This system overcomes such limitations by using simple video inputs from devices like smartphones, making avatar creation more accessible. It eliminates the need for high-definition cameras and studio visits, democratizing the creation and deployment of realistic avatars for broader user bases.
The system introduces AI-powered holographic avatars that act as real-time multilingual interpreters. These avatars utilize natural language processing for translation and present messages with realistic lip-syncing, expressions, and gestures. Users can customize these avatars, which are securely linked to their identities via blockchain protocols. The technology supports live and pre-recorded interactions across various platforms, enhancing communication systems with personalized, realistic avatars.
To address the challenges of creating custom large language models, the system offers a simplified framework for generating personalized models. Users can process large volumes of domain-specific data, such as religious texts and proprietary documents, to create license-free LLMs. This modular and privacy-preserving approach allows deployment on standard consumer hardware, removing barriers associated with traditional LLM development and making advanced AI solutions accessible to a wider audience.