Personalized Avatar Generation Using Depth Feature Classification
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Solution Overview
Problem
Current avatar generation systems fail to balance user likeness with design paradigms, often prioritizing one over the other, leading to inaccurate or time-consuming manual selection of user features.
Innovation Solution
A method and system that capture depth and color information to classify user features, align and wrap them into a generic mesh, and select corresponding avatar features to generate a personalized avatar that fits within a design paradigm while maintaining user likeness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If avatar features are selected from pre-stored options to match design paradigm, then design consistency is improved, but user likeness accuracy deteriorates
Solution Approach 1:
The system transforms continuous user feature parameters (captured from images) into discrete avatar feature parameters by mapping them to corresponding categories in the design paradigm. This parameter transformation allows the avatar to maintain design consistency while representing user characteristics through the available design options.
Solution Approach 2:
The patent replaces manual mechanical selection of avatar features with an automated image processing and classification system. The computer automatically analyzes user images, extracts feature characteristics, and selects appropriate avatar features, eliminating the need for manual intervention while balancing design constraints with user likeness.
2Measurement precision
If manual selection of avatar features is performed to achieve accurate user representation, then user likeness accuracy is improved, but time consumption increases
Solution Approach 1:
The system enables automatic self-service avatar generation by processing user images and selecting appropriate avatar features without requiring manual user input. The computer automatically performs feature extraction, classification, and avatar construction, significantly reducing the time users would otherwise spend manually configuring their avatars.
Solution Approach 2:
The patent replaces the manual mechanical process of selecting avatar features with an automated computer vision and machine learning system. The system automatically analyzes user images, classifies features, and generates the avatar, eliminating time-consuming manual operations while maintaining or improving accuracy.
3Ease of manufacture
If avatar features are selected from pre-stored options to follow design paradigm, then ease of manufacture is improved, but manufacturing precision deteriorates
Solution Approach 1:
The system changes the parameters of user features by mapping continuous real-world measurements to discrete design paradigm categories. This parameter transformation enables easy selection from pre-stored options while maintaining reasonable accuracy by intelligently mapping user characteristics to the nearest appropriate design options.
Solution Approach 2:
The patent replaces manual feature selection with an automated system that processes images and selects avatar features based on design paradigm constraints. This substitution makes the avatar generation process easier and more efficient while maintaining acceptable precision through algorithmic feature matching.
Data Source
AI summary
The present disclosure is related to a method to generate user representative avatars that fit within a design paradigm. The method includes receiving depth information corresponding to multiple user features of the user, determining one or more feature landmarks for the user based on the depth information, utilizing the one or more feature landmarks to classify a first user feature relative to an avatar feature category, selecting a first avatar feature from the avatar feature category based on the classification of the first user feature, combining the first avatar feature within an avatar representation to generate a user avatar, and output the user avatar for display.