Virtual Avatar Generation Using Neural Network Face Property Analysis
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Solution Overview
Problem
Current face feature point positioning technologies cannot accurately position finer feature information on images, limiting their ability to define detailed features such as eye contours and mouth contours.
Innovation Solution
A virtual avatar generation method and apparatus that uses a neural network to analyze face property features, determining a target virtual avatar template based on predefined correspondence between face property features and virtual avatar templates, allowing for more accurate face property analysis and generation of virtual avatars.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional face feature point positioning is used, then the positioning process is simple, but the measurement precision of fine feature information is insufficient
Solution Approach 1:
The patent segments the face analysis process into multiple specialized neural network models, each dedicated to specific feature types (eye features, mouth features, nose features, etc.). This segmentation allows each model to focus on extracting detailed features for particular facial regions, thereby improving measurement precision for fine feature information while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The patent introduces neural network models as intermediary components between the input face image and the final feature positioning output. These neural networks act as mediators that perform complex feature extraction and analysis, enabling high-precision positioning of fine features without requiring direct complex geometric calculations or manual feature detection
2Loss of information
If simple face features are defined, then the processing is fast, but the detail information is insufficient
Solution Approach 1:
By dividing the face analysis into separate neural network models for different feature types (eyes, mouth, nose, etc.), the system can process and retain detailed information for each feature category simultaneously. This segmentation prevents information loss while maintaining processing efficiency through parallel execution of specialized models
Solution Approach 2:
The patent applies partial action by using different neural network models with varying levels of complexity for different face features. Not all features require the same level of analysis depth, so the system applies appropriate analytical intensity to each feature type, preserving necessary detail information while optimizing overall processing speed
Data Source
AI summary
A virtual avatar generation method includes: determining a target task associated with at least one target face property, where the at least one target face property is one of a plurality of predefined face properties respectively; performing, according to the target task, target face property analysis on a target image including at least a face to obtain a target face property feature associated with the target face property of the target image; determining a target virtual avatar template corresponding to the target face property feature according to predefined correspondence between face property features and virtual avatar templates; and generating a virtual avatar of the target image based on the target virtual avatar template.