Optical Flow Cost Functions for 3D Face Modeling
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Solution Overview
Problem
Existing methods for generating a 3D morphable model of a user's face are inefficient in accurately matching the user's actual face, often resulting in multiple local minima when using conventional pixel-level intensity cost functions, making it difficult to find optimal parameter values for generating a precise 3D model.
Innovation Solution
The use of Principal Component Analysis (PCA) models combined with optical flow related cost functions to determine parameter values for shape and texture components, allowing for the generation of a 3D model that matches an input image within a given tolerance by iteratively searching through candidate parameter sets and projecting the 3D model onto a 2D surface, thereby avoiding local minima issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pixel-level intensity cost functions are used to determine parameter values, then the process is simple to implement, but multiple local minima occur making it difficult to find optimal parameters
Solution Approach 1:
The patent changes the parameter space by transforming the optimization problem from direct pixel intensity matching to optical flow field matching. This parameter transformation converts a discrete, noisy optimization landscape into a continuous, smooth one, enabling more reliable convergence to optimal parameters while maintaining implementation feasibility through established optical flow algorithms.
Solution Approach 2:
The patent introduces optical flow as an intermediary between the 3D model and the input image. Instead of directly comparing pixel intensities, the optical flow field serves as a mediator that captures motion patterns and structural relationships, providing a more robust intermediate representation for parameter optimization and reducing the occurrence of local minima.
2Manufacturing precision
If optical flow related cost functions are used to determine parameter values, then accuracy and efficiency of 3D face modeling is improved, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary computation of the optical flow field between the input image and synthesized images before the actual parameter optimization. This pre-computation step prepares the cost function data in advance, organizing it in a form that facilitates efficient optimization and reduces the computational burden during the iterative parameter adjustment phase, thereby improving overall efficiency.
Solution Approach 2:
The patent replaces traditional mechanical intensity-based matching mechanisms with optical flow-based computational mechanisms. By substituting direct pixel comparison with optical flow field analysis, the system achieves higher precision in parameter determination while leveraging efficient computational algorithms to manage the complexity of the transformation.
3Measurement precision
If multiple candidate parameter sets are searched to find optimal parameters, then model accuracy is improved, but the time required for parameter determination increases
Solution Approach 1:
The patent implements a feedback mechanism where the optical flow cost function provides continuous performance information during the parameter optimization process. This feedback guides the search through candidate parameter sets by indicating the quality of each candidate, enabling more intelligent and efficient exploration of the parameter space and reducing the time required to identify optimal parameters while maintaining high model accuracy.
Data Source
AI summary
Modeling of a user's face is disclosed, including: receiving an input image of a user's face to be modeled; and generating a set of parameter values to a statistical model that corresponds to the input image by evaluating candidate parameter values using a cost function that is determined based at least in part on optical flow.