3D Model Generation from Single 2D Image Using Neural Networks
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Solution Overview
Problem
Existing methods for generating 3D models of objects, particularly human bodies, from a single 2D image fail to capture finer details in shape and facial features, and require multiple images or studio environments.
Innovation Solution
A method using neural networks to measure geometrical shape coordinates and texture parameters from a single 2D image, predict occluded portions, and generate a 3D model by mapping these coordinates and parameters, enhancing the model through comparison with pre-learned ground truth models, and adding liveliness to specific parts like faces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing methods use multiple images or studio environments to generate 3D models, then the completeness and accuracy of the model is improved, but the complexity of the process and equipment requirements increase
Solution Approach 1:
The patent segments the 3D model generation process into distinct neural network components: a first neural network for measuring geometrical shape coordinates, a second neural network for identifying texture parameters, and a third neural network for predicting occluded portions. This segmentation allows each component to specialize in specific tasks, improving overall accuracy while maintaining operational simplicity through automated processing.
Solution Approach 2:
The patent replaces complex mechanical capture systems (multiple cameras, studio environments, controlled lighting) with an AI-based neural network system that processes single 2D images. This substitution eliminates the need for specialized equipment and environments while achieving comparable or superior model quality through computational intelligence.
2Ease of operation
If AI generators are used to create 3D models from single images, then the ease of operation is improved, but the detail quality and realism of the model deteriorates
Solution Approach 1:
The patent applies local quality by treating different regions of the 3D model with different processing approaches. The system specifically enhances facial regions and occluded portions through specialized prediction modules, while maintaining efficient processing for visible areas. This allows detailed quality in critical regions without compromising overall operational simplicity.
Solution Approach 2:
The patent performs preliminary action by training multiple specialized neural networks in advance on extensive datasets. The first neural network is pre-trained for geometrical coordinate extraction, the second for texture parameter identification, and the third for occluded portion prediction. This preliminary training enables the system to deliver high-detail results during actual operation without requiring complex real-time processing.
3Loss of information
If traditional methods capture all visible surfaces, then the completeness of the model is improved, but the difficulty of capturing occluded portions increases
Solution Approach 1:
The patent introduces an intermediary neural network (the third neural network) that acts as a mediator between the visible 2D image and the complete 3D model. This intermediary predicts and generates the missing occluded portions by learning from training data, effectively bridging the information gap without requiring direct capture of hidden surfaces.
Solution Approach 2:
The patent uses copying by training the third neural network on datasets containing both visible and occluded portions of objects. The network learns to copy the characteristics and patterns of occluded regions from training examples, enabling it to generate accurate predictions for hidden areas without direct observation during the actual scanning process.
Data Source
AI summary
A method for generating a three-dimensional (3D) model of an object includes receiving a two-dimensional (2D) view of at least one object as an input, measuring geometrical shape coordinates of the at least one object from the input, identifying texture parameters of the at least one object from the input, predicting geometrical shape coordinates and texture parameters of occluded portions of the at least one object in the 2D view by processing the measured geometrical shape coordinates of the at least one object, the identified texture parameters of the at least one object, and the occluded portions of the at least one object, and generating a 3D model of the at least one object by mapping the measured geometrical shape coordinates and the identified texture parameters to the predicted geometrical shape coordinates and the predicted texture parameters of the occluded portions of the at least one object.