Emotion-Responsive Animated Character Video Generation
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Solution Overview
Problem
Conventional customer service systems using virtual digital people can only provide simple human-computer interaction, lacking emotional responses, resulting in a poor user experience due to reliance on only speech recognition and semantic understanding.
Innovation Solution
A method and apparatus for human-computer interaction that receives and analyzes multi-modal user information, including image and audio data, to recognize user intentions and emotional characteristics, generating a broadcast video of an animated character image that responds emotionally to users by integrating speech and expression emotion recognition models with a pre-established animated character image model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple speech recognition and semantic understanding are used, then the system complexity is low, but the emotional response capability and user interaction experience deteriorate
Solution Approach 1:
The system segments the emotion recognition process into multiple independent modules: speech emotion recognition model, expression emotion recognition model, and text emotion recognition model. Each module processes one type of input data separately, then their results are combined through weighted summation. This segmentation allows the system to achieve comprehensive emotional understanding without creating a single overly complex monolithic system.
Solution Approach 2:
The system merges multiple emotion recognition approaches (speech, expression, text) into a unified emotion analysis framework. By combining the outputs of different recognition models through weighted summation, the system achieves more accurate and reliable emotional understanding than any single approach could provide alone, while maintaining modular architecture.
2Measurement precision
If multi-modal information analysis is implemented, then the emotional analysis accuracy is improved, but the device complexity increases
Solution Approach 1:
The system divides multi-modal input processing into separate specialized modules: one for speech data, one for image/expression data, and one for text data. Each module has its dedicated emotion recognition model trained for that specific modality. This segmentation improves accuracy for each modality while keeping individual module complexity manageable.
Solution Approach 2:
The system creates a universal emotion recognition framework that can process multiple types of input data (speech, images, text) through a common architecture. The weighted summation mechanism provides a universal method for combining results from different modalities, making the system adaptable to various input types without requiring completely separate processing pipelines.
3Ease of operation
If emotional feedback through animated character video is provided, then the user interaction experience is improved, but the information processing time and system complexity increase
Solution Approach 1:
The system performs preliminary processing of user inputs through parallel emotion recognition models that can quickly analyze speech, expressions, and text simultaneously. By preparing emotion analysis results before generating the animated character response, the system reduces the overall processing time required to deliver emotional feedback.
Solution Approach 2:
The system uses pre-established animated character image models that can be rapidly instantiated and animated based on the detected emotional state. Instead of generating complex animations from scratch, the system copies and adapts pre-defined character models and expressions, significantly reducing the time required to produce emotional feedback videos.
Data Source
AI summary
A human-computer interaction method and apparatus. Said method may include: receiving information of at least one modality of a user (201); identifying, on the basis of the information of the at least one modality, intention information of the user and user emotional features corresponding to the intention information (202); determining, on the basis of the intention information, reply information to the user (203); selecting, on the basis of the user emotional features, character emotional features to be fed back to the user (204); and generating, on the basis of the character emotional features and the reply information, a broadcast video of an animated character corresponding to the character emotional features (205).