3D Face Model Generation from Single Image
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Solution Overview
Problem
Current three-dimensional montage generation methods are inefficient and inaccurate, as they require multiple photographs and consume significant time and resources, failing to effectively generate models that reflect depth information from a single two-dimensional front face image.
Innovation Solution
A system and method that uses a two-dimensional single image to generate a three-dimensional montage by extracting feature points, estimating depth information using a K-Nearest Neighbor algorithm, and transforming a three-dimensional template model through a blend shape technique, allowing for rapid and accurate creation of three-dimensional models from a single front face photograph.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple photographs are used to generate three-dimensional montage, then depth information accuracy is improved, but processing time and resource consumption increase
Solution Approach 1:
The patent uses a pre-established three-dimensional template model as a copy of typical facial structures. Instead of creating a new 3D model from multiple photographs, the system copies and adapts the template model to match the input single photograph, significantly reducing processing time while maintaining depth information accuracy through the template's pre-defined geometric relationships
Solution Approach 2:
The system changes parameters by adjusting the template model's geometric parameters (such as nose height, eye contour, lip protrusion) to match the feature points extracted from the input photograph. This parameter adaptation allows the template to reflect accurate depth information without requiring multiple input photographs or complex reconstruction processes
2Manufacturing precision
If optimization algorithm is used to approximate facial looks, then three-dimensional model accuracy is improved, but processing time increases to two or three hours
Solution Approach 1:
The patent performs preliminary action by pre-establishing the three-dimensional template model with accurate geometric relationships and facial structure data before actual montage generation. This pre-computation eliminates the need for time-consuming optimization algorithms during runtime, as the template is already optimized and ready for rapid parameter adaptation
Solution Approach 2:
Instead of running optimization algorithms to approximate facial looks, the system copies the pre-optimized template model and adjusts its parameters to match the input photograph. This copying approach inherits the template's accurate geometric relationships without requiring iterative optimization, reducing processing time from hours to minutes
3Productivity
If simple warping technology is used for three-dimensional face generation, then processing speed is improved, but model naturalness deteriorates
Solution Approach 1:
The patent transitions from two-dimensional warping to three-dimensional template-based modeling. By working in three-dimensional space with a structured template model, the system maintains geometric accuracy and naturalness while achieving rapid processing through parameter adaptation rather than pixel-level warping operations
4Manufacturing precision
If manual endeavor by animators is used for model creation, then model quality is improved, but time and cost consumption increase
Solution Approach 1:
The patent replaces manual animator work with automated copying of the three-dimensional template model. The template encapsulates high-quality facial geometry created in advance, and the system copies and adapts this template through automated feature point matching, eliminating the need for manual animation work while maintaining model quality
Solution Approach 2:
The system performs self-service by automatically extracting feature points from the input photograph and adapting template parameters without human intervention. This automation replaces manual animator endeavors, reducing both time and cost while maintaining consistent model quality through algorithmic parameter optimization
Data Source
AI summary
The present disclosure relates to a three-dimensional montage generation system and method based on a two-dimensional single image. An embodiment of the present disclosure may generate a three-dimensional montage in an easy, fast and accurate way by using a two-dimensional front face image data, and estimate face portions, which cannot be restored by using a single photograph, in a statistic way by using a previously prepared face database. Accordingly, an embodiment of the present disclosure may generate a three-dimensional personal model from a single two-dimensional front face photograph, and depth information such as nose height, lip protrusion and eye contour may be effectively estimated by means of statistical distribution and correlation of data.