3D Human Model Refinement Using Neural Network Parameter Adjustment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing 3D human models generated using pre-trained neural networks may not accurately represent a patient's real pose and shape, leading to estimation bias and prediction errors, especially when the patient's body shape deviates from the average shape in the training dataset.
Innovation Solution
The system utilizes processors to obtain 3D models of patients using images, determining key body locations and shapes, and adjusts these models by refining neural network parameters based on additional information from images, such as key body locations and depth maps, to minimize differences and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pre-trained neural networks are used to generate 3D human models, then the generation process is fast and automated, but the accuracy of pose and shape representation deteriorates when patient body shape deviates from average training data
Solution Approach 1:
The system performs preliminary actions by capturing multiple 2D images and depth maps of the patient before 3D model generation, and pre-processing this data to identify body shape characteristics and pose information. This preliminary data collection and analysis enables the subsequent refinement process to accurately adjust the 3D model parameters, resolving the contradiction between fast automated generation and accurate representation of non-average body shapes.
Solution Approach 2:
The system implements feedback mechanisms by comparing the generated 3D model against multiple independently determined representations (key body locations from 2D images, body shape from depth maps, and anatomical constraints). The model parameters are iteratively adjusted based on this feedback to minimize differences, ensuring high accuracy while maintaining automated processing through algorithmic optimization.
2Measurement precision
If multiple independent representations (key body locations, body shape) are determined and used to adjust the 3D model, then the accuracy and personalization of the model improves, but the computational complexity and processing time increases
Solution Approach 1:
The system segments the complex task of 3D model adjustment into multiple independent sub-tasks: determining key body locations from 2D images, extracting body shape from depth maps, identifying anatomical constraints, and adjusting specific model parameters. Each segment can be processed independently and in parallel, reducing overall computational complexity while maintaining high accuracy through comprehensive multi-faceted adjustment.
Solution Approach 2:
The system changes parameters of the 3D model (pose parameters, shape parameters, key point locations) based on multiple independent representations. By adjusting these parameters iteratively to minimize differences between the generated model and reference measurements, the system achieves high accuracy without requiring completely complex processing architectures, as parameter optimization leverages mathematical efficiency.
3Measurement precision
If the 3D model is adjusted to minimize differences between multiple representations, then the personalized accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis of the captured images and depth maps to identify key body locations, body shape characteristics, and anatomical constraints before the iterative adjustment process. This pre-processing organizes and pre-computes critical information, reducing the computational burden during the actual model adjustment phase and enabling faster convergence to an accurate personalized model.
Solution Approach 2:
The system efficiently adjusts model parameters by directly optimizing pose and shape parameters based on the pre-computed reference measurements. By formulating the adjustment as a parameter optimization problem with clear objective functions (minimizing differences between model predictions and reference data), the system achieves rapid convergence using gradient-based optimization methods, balancing accuracy with processing time.
Data Source
AI summary
A three-dimensional (3D) model of a person may be obtained using a pre-trained neural network based on one or more images of the person. Such a model may be subject to estimation bias and/or other types of defects or errors. Described herein are systems, methods, and instrumentalities for refining the 3D model and/or the neural network used to generate the 3D model. The proposed techniques may extract information such as key body locations and/or a body shape from the images and refine the 3D model and/or the neural network using the extracted information. In examples, the 3D model and/or the neural network may be refined by minimizing a difference between the key body locations and/or body shape extracted from the images and corresponding key body locations and/or body shape determined from the 3D model. The refinement may be performed in an iterative and alternating manner.