3D Face Parameter Estimation Using Weighted Multi-Loss Training
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Solution Overview
Problem
Existing methods for creating realistic three-dimensional face models from two-dimensional face images face challenges in accurately estimating reconstruction parameters due to the complexity and requirement of additional sensors, leading to incomplete or inaccurate three-dimensional face reconstructions.
Innovation Solution
A method involving a neural network model trained on a face image training set to estimate reconstruction parameters, using loss functions and weights to adjust and optimize the parameter estimation model, allowing for accurate three-dimensional face reconstruction without additional three-dimensional face information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If pixel values of feature points are directly determined as supervision information to estimate reconstruction parameters, then the estimation process is simplified, but the accuracy of estimating reconstruction parameters cannot be ensured
Solution Approach 1:
The patent introduces a parameter estimation model as an intermediary component that processes two-dimensional face images and outputs reconstruction parameters. This model is trained using a comprehensive loss function that combines multiple supervision information sources (feature point pixel values, face image regions, and depth information) to accurately estimate reconstruction parameters without requiring direct complex transformations from 2D to 3D space.
2Measurement precision
If multiple face images and depth information are collected to estimate reconstruction parameters, then the accuracy of reconstruction parameters is improved, but the collection requirements become excessive and operation complexity increases
Solution Approach 1:
The patent designs a parameter estimation model that can process various types of input data (two-dimensional face images, depth information, and feature point pixel values) through a unified framework. The model uses a composite loss function that integrates multiple supervision information sources, allowing it to accurately estimate reconstruction parameters from diverse data types without requiring separate specialized collection processes for each data type.
3Adaptability or versatility
If a three-dimensional morphable model is constructed with multiple principal component bases to represent face diversity, then the completeness of three-dimensional face information is improved, but the complexity of reconstructing three-dimensional faces increases
Solution Approach 1:
The patent segments the complex three-dimensional face reconstruction problem into multiple independent components: a three-dimensional morphable model for geometric representation, a parameter estimation model for calculating reconstruction parameters, and a loss function for optimization. The parameter estimation model independently processes the segmentation of face features and expressions through separate principal component bases, then combines them to produce the final three-dimensional face reconstruction, simplifying the overall reconstruction process while maintaining completeness.
Data Source
AI summary
Provided is a method for training parameter estimation models. The method includes: estimating reconstruction parameters specified for three-dimensional face reconstruction by inputting training samples in a face image training set to a pre-constructed neural network model, and reconstructing three-dimensional faces corresponding to the training samples by inputting the reconstruction parameters to a pre-constructed three-dimensional morphable model; calculating a plurality of loss functions of a plurality of pieces of two-dimensional supervision information between the three-dimensional faces and the training samples, and adjusting weights corresponding to the plurality of loss functions; and generating fitting loss functions based on the plurality of loss functions and the weights corresponding to the plurality of loss functions, and acquiring a trained parameter estimation model by performing an inverse correction on the neural network model using the fitting loss functions.