3D Facial Synthesis with Pseudogene Trait Inheritance
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Solution Overview
Problem
Current methods for generating 3D face models from 2D face images result in low accuracy and efficiency due to the lack of spatial information in 2D images, and manual or scanning-based modeling techniques are inefficient.
Innovation Solution
A facial synthesis method that utilizes trait inheritance rules based on pseudogenes to combine 3D face models, allowing for the synthesis of high-quality 3D face models by integrating facial features from multiple models, using generative adversarial networks and diffusion models for improved accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If 3D face models are synthesized using traditional 2D face image methods, then the process is simple, but the accuracy of the synthesized 3D face model is low due to lack of spatial information
Solution Approach 1:
The patent segments the 3D face model synthesis process into multiple independent components: extracting facial features from parent models, determining pseudogene categories for each feature, applying inheritance rules, and synthesizing child models. This segmentation allows parallel processing of multiple features and models, improving efficiency while maintaining accuracy through systematic trait inheritance.
Solution Approach 2:
The patent performs preliminary actions by pre-extracting facial features from parent 3D models and pre-determining pseudogene categories before the actual synthesis process. This preparation work is done once and reused across multiple child model generations, significantly improving efficiency while ensuring consistent accuracy through predetermined inheritance rules.
2Productivity
If multiple 3D face models are synthesized manually or through scanning, then high accuracy can be achieved, but the efficiency and time consumption are very low
Solution Approach 1:
The patent uses digital copying of facial features from parent 3D models rather than physical scanning. Facial features are extracted as digital data and replicated across multiple child models through computational processes. This digital copying approach enables rapid generation of multiple models with consistent accuracy without the time-consuming physical scanning process.
Solution Approach 2:
The patent replaces mechanical scanning and manual modeling processes with computational algorithms. Instead of physical scanners and manual 3D modeling, the system uses automated feature extraction, pseudogene categorization, and inheritance-based synthesis algorithms to generate 3D face models, dramatically improving efficiency while reducing system complexity.
3Manufacturing precision
If trait inheritance rules based on pseudogenes are applied to 3D face model synthesis, then accuracy and explainability are improved, but the complexity of the synthesis process increases
Solution Approach 1:
The patent changes the parameter representation of facial features by introducing pseudogene categories (dominant, recessive, co-dominant) as standardized parameters. This parameter transformation allows complex inheritance patterns to be represented through simple categorical values, making the system more manageable and explainable while maintaining high synthesis accuracy.
Solution Approach 2:
The patent applies local quality by assigning different pseudogene categories to different facial features based on their specific inheritance characteristics. Each facial feature can have its own inheritance pattern (dominant, recessive, or co-dominant), allowing accurate representation of biologically diverse trait inheritance while keeping the overall system structured and manageable through localized categorization.
Data Source
AI summary
This application provides a facial synthesis method and apparatus. In embodiments, first facial information of a first model and second facial information of a second model are obtained, where facial information includes category information of a trait of each facial feature of a face. Third facial information is synthesized based on the first facial information and the second facial information according to a trait inheritance rule, where the third facial information corresponds to a third model, and the trait inheritance rule indicates a synthesis coefficient corresponding to the category information of the trait of the facial feature. Therefore, accuracy of 3D face model synthesis is ensured, and efficiency of 3D face model synthesis is improved.