3D Digital Human Generation Using Face Key Point Refinement
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Solution Overview
Problem
Existing methods for generating three-dimensional digital humans face accuracy and efficiency issues due to uneven quality of two-dimensional images, requiring manual refinement and increasing model layers, which are costly and not universally effective.
Innovation Solution
Perform key point detection on a face image to obtain specific key point data, determine a face feature vector, generate initial digital face data, and update it with the key point data to improve accuracy, using neural networks for feature extraction and rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual face refining is performed to improve accuracy, then the accuracy of three-dimensional digital face is improved, but the efficiency of generating three-dimensional digital human is reduced
Solution Approach 1:
The system performs automatic face refining using neural networks and key point detection algorithms, allowing the process to self-improve without manual intervention. The algorithm automatically adjusts and refines the three-dimensional digital face based on detected key points and feature vectors, eliminating the need for manual refining operations while maintaining high accuracy.
Solution Approach 2:
The patent replaces manual mechanical refining operations with automated computational algorithms. Specifically, it uses neural networks to process two-dimensional images and generate three-dimensional digital faces, substituting the manual mechanical adjustment process with automated digital processing that achieves both high accuracy and efficiency.
2Manufacturing precision
If model layers are increased to improve accuracy, then the accuracy of three-dimensional digital face is improved, but the cost and complexity of the system is increased
Solution Approach 1:
The patent segments the face generation process into distinct functional modules: key point detection module, feature vector extraction module, and three-dimensional digital face generation module. Each module performs a specific function independently, making the system more manageable and less complex while maintaining high accuracy through specialized processing at each stage.
Solution Approach 2:
The patent introduces key point data and feature vectors as intermediary representations between the two-dimensional image and the three-dimensional digital face. These intermediaries serve as bridges that enable accurate transformation without requiring complex direct mapping models, simplifying the overall system architecture while maintaining precision.
Data Source
AI summary
The method includes: performing a key point detection on a face image to be processed to obtain first specific key point data; determining a first face feature vector corresponding to the face image to be processed; generating initial digital face data based on the first face feature vector, and updating the initial digital face data with the first specific key point data to obtain three-dimensional digital face data; and processing the three-dimensional digital face data by digital human generation software to obtain a target three-dimensional digital human.