3D Face Replacement Feature Fusion for Identity and Contour Consistency
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Solution Overview
Problem
Existing face image processing methods focus solely on retaining identity, leading to low accuracy in face image processing, particularly in applications requiring style retention and face replacement.
Innovation Solution
Perform three-dimensional face modeling to extract and fuse face features, followed by transformation and replacement to maintain face contours and identity, using machine learning networks for accurate face replacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If face image processing methods only consider retaining identity, then identity consistency is maintained, but processing accuracy deteriorates
Solution Approach 1:
The patent segments face processing into distinct modules: identity retention module, style transfer module, and contour preservation module. Each module handles specific aspects independently, allowing identity consistency to be maintained while improving overall processing accuracy through specialized processing of different face attributes
Solution Approach 2:
The patent introduces three-dimensional face modeling to add a spatial dimension to traditional two-dimensional face processing. By extracting three-dimensional face features and using them in the replacement process, the system achieves both identity consistency and improved accuracy in maintaining face contours and styles
2Manufacturing precision
If three-dimensional face modeling is performed to extract and fuse face features, then face contour consistency is improved, but computational complexity increases
Solution Approach 1:
The patent performs three-dimensional face modeling and feature extraction as preliminary actions before the actual face replacement. By pre-processing the source and template faces to extract three-dimensional features and generate transformation matrices, the complex computations are done in advance, reducing the computational burden during the replacement process itself
Solution Approach 2:
The patent uses three-dimensional fusion features as an intermediary that bridges the source face and template face. This intermediate representation consolidates the complex three-dimensional information into a unified feature set that can be efficiently used for transformation and replacement, reducing the complexity of direct manipulation
3Measurement precision
If face replacement feature extraction is performed based on template face, then replacement accuracy is improved, but processing time increases
Solution Approach 1:
The patent replaces traditional pixel-based mechanical image processing with machine learning-based feature extraction and transformation. By using neural networks to learn optimal feature representations and transformation patterns from training data, the system achieves high replacement accuracy while reducing processing time through efficient learned operations
Data Source
AI summary
This application discloses a face image processing method performed by an electronic device. The method includes: acquiring a face image of a source face and a face template image of a template face; performing three-dimensional face modeling on the face image and the face template image to obtain a three-dimensional face image feature of the face image and a three-dimensional face template image feature of the face template image; fusing the three-dimensional face image feature and the three-dimensional face template image feature to obtain a three-dimensional fusion feature; performing face replacement feature extraction on the face image based on the face template image to obtain an initial face replacement feature; transforming the initial face replacement feature based on the three-dimensional fusion feature to obtain a target face replacement feature; and replacing the template face with the source face based on the target face replacement feature to obtain a target face image.