Emovector Virtual Representative Emotional Expression Control
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Solution Overview
Problem
Traditional virtual reality systems lack the ability to reproduce emotional expressions and body movements of users, limiting the authenticity of user interactions in virtual environments.
Innovation Solution
A method and system that generate a virtual representative using visual data and an emovector, which is processed by a machine learning model to control the virtual representative's emotional expressions and body movements, enabling authentic user representation in virtual environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a traditional avatar system is used with chatbot and text-to-speech, then audio feedback is enabled, but emotional expressions and facial expressions cannot be reproduced
Solution Approach 1:
The patent introduces an emovector as an intermediary data structure that captures emotional expression information from visual data. This emovector serves as a mediator between the user's facial expressions and the virtual representative's emotional display, enabling emotional information transfer without requiring complex direct analysis systems.
Solution Approach 2:
The system creates a virtual representative that copies the user's emotional expressions by generating an emovector from visual data and applying it to control the virtual representative's facial expressions and body movements. This copying approach preserves emotional information while operating in the virtual environment.
2Measurement precision
If visual data processing is added to capture emotional expressions, then emotional representation is improved, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary processing by extracting emotional expression data from visual input and encoding it into an emovector structure before the virtual representative needs to display the emotions. This pre-processing approach prepares emotional data in advance, reducing real-time computational burden during interaction.
Solution Approach 2:
The patent transforms complex visual data into a simplified emovector representation with specific parameters that capture essential emotional information. This parameter transformation reduces data complexity while preserving emotional expression accuracy, enabling efficient processing.
Data Source
AI summary
Techniques are provided for implanting emovectors with virtual representatives. In one embodiment, the techniques involve generating a virtual representative based on visual data of a first user, generating an emovector of a second user of a virtual environment, generating an input of a machine learning model based on the emovector of the second user, and controlling the virtual representative based on an output of the machine learning model.