Machine-Generated Avatars With Speech-Facial Sync Modeling
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Solution Overview
Problem
Existing avatars lack lifelike speech and facial expressions, leading to a disparity between spoken words and facial movements, which detracts from the immersive experience.
Innovation Solution
A machine-generated avatar is created using machine learning algorithms to process video and audio information, extracting data for predictive models that generate lifelike speech and facial expressions by analyzing pixel, semantic, phonetic, and intonation patterns, enabling the avatar to produce unlimited utterances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If generic prerecorded responses are used for avatar speech, then implementation simplicity is maintained, but speech naturalness and facial expression synchronization deteriorate
Solution Approach 1:
The patent replaces traditional mechanical approaches (prerecorded audio files, manual lip-sync animation) with a neural network-based system that automatically generates speech and synchronizes facial expressions. The neural network learns from training data to produce natural speech and corresponding viseme sequences, eliminating the need for manual recording and synchronization work.
Solution Approach 2:
The system creates a digital copy of human speech and facial expression patterns through neural network training. By learning from extensive training corpora of human speech and corresponding video footage, the system replicates natural speech patterns and facial movements, generating unlimited utterances that mimic human communication without requiring actual human performers for each scenario.
2Adaptability or versatility
If unlimited utterances are generated through machine learning, then speech versatility improves, but system complexity increases
Solution Approach 1:
The system performs preliminary action through an extensive training phase where neural networks are pre-trained on large corpora of speech and video data. This pre-training establishes the foundational models for speech generation and viseme prediction, enabling the system to handle unlimited utterances afterward without requiring complex real-time processing for each new speech act.
Solution Approach 2:
The patent segments the speech generation process into distinct modular components: speech-to-text conversion, phoneme extraction, viseme sequence generation, and video frame synthesis. Each component is handled by specialized neural networks or processing modules, allowing the system to manage complexity through functional decomposition while maintaining high versatility.
3Reliability
If facial expressions are synchronized with spoken words using machine learning, then user immersion improves, but processing requirements increase
Solution Approach 1:
The patent replaces computationally intensive real-time video analysis and manual animation systems with pre-trained neural networks that predict viseme sequences from speech input. The neural networks have already learned the complex mappings between speech patterns and facial movements during training, enabling efficient real-time generation without requiring heavy processing resources during actual avatar operation.
Data Source
AI summary
Systems and methods are disclosed for creating a machine generated avatar. A machine generated avatar is an avatar generated by processing video and audio information extracted from a recording of a human speaking a reading corpora and enabling the created avatar to be able to say an unlimited number of utterances, i.e., utterances that were not recorded. The video and audio processing consists of the use of machine learning algorithms that may create predictive models based upon pixel, semantic, phonetic, intonation, and wavelets.