Virtual Character Control Mode Switching for Flexible Output
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Solution Overview
Problem
Current virtual human control systems rely solely on data collected by action sensors worn by a person, limiting flexibility and variety in controlling virtual characters.
Innovation Solution
Implementing multiple modes of control for virtual characters, including action data matching operator actions and response results, with automatic mode switching based on predefined conditions, to enhance flexibility and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If virtual character control relies solely on action sensor data, then the control system is simple, but the flexibility and adaptability are limited
Solution Approach 1:
The patent implements dynamic mode switching between first mode (action sensor data-driven) and second mode (response data-driven) control. The system adapts its control approach based on real-time conditions such as data confidence levels and action constraints, allowing the virtual character to transition flexibly between different control paradigms rather than relying on a single static control method.
Solution Approach 2:
The system changes the control parameter source dynamically - switching between action sensor data (first mode) and response data from large language models (second mode) based on confidence thresholds and constraint satisfaction. This parameter change enables the system to adapt to different operational contexts while managing complexity through conditional logic.
2Reliability
If multiple control modes are implemented, then the service responsiveness improves, but the control complexity increases
Solution Approach 1:
The system employs feedback mechanisms by evaluating data confidence levels and action constraint satisfaction to determine when to switch between control modes. The first mode uses action sensor data with confidence assessment, while the second mode activates when constraints are not satisfied, creating a feedback-driven adaptive control system that improves reliability through conditional mode selection.
Solution Approach 2:
The control system is segmented into distinct modes: first mode for action sensor-driven control and second mode for response data-driven control. This segmentation allows each mode to be optimized for specific scenarios, with clear transition criteria based on data confidence and constraint satisfaction, making the overall complex system manageable through modular design.
3Adaptability or versatility
If action sensor data is used for control, then the control response is direct, but the adaptability to different scenarios is limited
Solution Approach 1:
The system performs preliminary assessment of data confidence and constraint satisfaction before executing control actions. By pre-evaluating whether action sensor data meets confidence thresholds and constraint requirements, the system can quickly determine the appropriate control mode without extensive processing during execution, reducing time loss while enhancing scenario adaptability.
Solution Approach 2:
The patent introduces response data from large language models as an intermediary control source that bridges the gap between limited action sensor capabilities and diverse scenario requirements. This intermediary provides additional contextual understanding and adaptive response generation, enabling the virtual character to handle varied scenarios beyond what action sensors alone can support.
Data Source
AI summary
An output method includes controlling a virtual character to perform action according to a first mode and controlling the virtual character to perform action according to a second mode. The second mode is different from the first mode.