Listener Animation Personalization via Style Embeddings
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Solution Overview
Problem
Current electronic devices lack the ability to effectively generate personalized and dynamic listener animations for virtual assistants, which limits user interaction and engagement in voice-controlled environments.
Innovation Solution
A system that uses machine learning models to process audio and image data to generate unique facial animations based on listener styles, allowing for personalized and responsive listener animations without the need for extensive training data for each user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional animation methods are used for virtual assistants, then the system is simple to implement, but the user engagement and personalization are insufficient
Solution Approach 1:
The system creates a virtual listener avatar that copies and responds to user speech patterns, tone, and content in real-time. The avatar's facial expressions and gestures are generated by analyzing audio features and mapping them to corresponding visual animations, providing personalized interaction without requiring complex custom training for each user scenario
Solution Approach 2:
The patent replaces traditional mechanical animation systems with AI-driven computational models. Instead of using pre-programmed animations or simple keyframe systems, the invention uses machine learning models that process audio data and generate dynamic facial animations through neural networks, substituting mechanical animation with intelligent computation
2Measurement precision
If extensive training data is collected for each user, then the animation accuracy is improved, but the data processing time and system complexity increase
Solution Approach 1:
The system performs preliminary action by pre-training a general listener animation model on diverse speech patterns and expressions before actual use. This pre-trained model serves as a foundation that can quickly adapt to individual users without requiring extensive retraining, thereby reducing real-time processing time while maintaining high animation accuracy
Solution Approach 2:
The invention changes parameters by using transfer learning techniques where a general model is fine-tuned with minimal user-specific data. Instead of training from scratch for each user, the system adjusts existing model parameters based on individual user characteristics, achieving high accuracy with significantly reduced training time and computational resources
3Productivity
If real-time audio and image processing is implemented, then the user interaction is enhanced, but the processing speed and computational requirements decrease
Solution Approach 1:
The system segments the processing task into distinct modules: audio feature extraction, speech intent recognition, facial expression synthesis, and animation rendering. Each module operates independently and can be optimized separately, allowing real-time processing by dividing the computational workload into manageable segments that process data in parallel
Data Source
AI summary
A system configured to perform style-aware listener animation. By representing different listening styles (e.g., facial expressions) using an embedding space, a single model can be trained to generate unique facial animations for a number of distinct listeners. Thus, individual listening styles can be associated with a listener identifier, enabling the system to (i) animate a plurality of different listeners with unique nonverbal behavior and/or (ii) select a particular listener identifier or desired type of listener style with which to animate. This enables the model to be generalized to new listeners to generate additional listener facial responses without needing training data for each new listener. The model may process a listener representation style or listener identifier, along with input data corresponding to a speaker talking, to generate unique facial animation responsive to the speech.