Hand Calibration Using Single Depth Camera and Neural Network
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Solution Overview
Problem
Existing hand tracking technologies face challenges in accurately and efficiently determining user hand poses from images, particularly in recognizing variations in hand size and shape across different users, while also requiring high computational speed and reduced hardware resources.
Innovation Solution
A user hand shape model is generated using a limited number of principal component hand shape models, which are combined to reduce the number of variables needed for hand shape determination, and a neural network processes depth image data to identify hand features and poses, facilitating rapid calibration and subsequent pose determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional hand tracking methods are used to accurately recognize variations in hand size and shape across different users, then measurement precision is improved, but computational time increases and hardware requirements increase
Solution Approach 1:
The patent transforms the hand shape representation from a high-dimensional parameter space to a low-dimensional principal component space. By representing hand shapes as linear combinations of a small number of principal component models (e.g., 10-20 components instead of hundreds of vertices), the system maintains measurement precision while dramatically reducing computational time and complexity.
Solution Approach 2:
The patent creates simplified copies of hand shapes using principal component models that capture the essential variations in hand geometry. These simplified models serve as proxies for detailed hand meshes, enabling fast calibration and pose estimation without requiring full high-fidelity hand models, thus reducing computational overhead while preserving accuracy.
2Measurement precision
If traditional hand tracking methods are used to accurately recognize variations in hand size and shape across different users, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent reduces device complexity by changing the parameter representation from high-dimensional vertex coordinates to low-dimensional principal component coefficients. This transformation allows the system to achieve the same measurement precision with simpler hardware and less computational resources, as the calibration and tracking algorithms operate in a reduced parameter space.
Solution Approach 2:
The patent extracts only the essential variations in hand shape by computing principal components from training data. By taking out and retaining only the most significant modes of variation (e.g., size, aspect ratio, finger length differences), the system eliminates redundant parameters and simplifies the device requirements while maintaining the ability to accurately represent individual hand characteristics.
3Productivity
If a limited number of principal component hand shape models are used to reduce variables for rapid determination of hand shape, then productivity is improved, but measurement precision may worsen
Solution Approach 1:
The patent resolves this contradiction by carefully selecting the number of principal components to retain. By analyzing the eigenvalues and variance explained by each component, the system identifies an optimal cutoff point where a small number of components (e.g., 10-20) captures sufficient hand shape variation for accurate calibration and tracking, achieving both high productivity and maintained precision.
Solution Approach 2:
The patent applies partial action by using only the most significant principal components needed for accurate hand shape determination. Rather than using all available components, the system selectively employs a subset that provides sufficient precision for the application, thereby achieving rapid calibration (high productivity) without sacrificing necessary measurement accuracy.
Data Source
AI summary
A system generates a user hand shape model from a single depth camera. The system includes the single depth camera and a hand tracking unit. The single depth camera generates single depth image data of a user's hand. The hand tracking unit applies the single depth image data to a neural network model to generate heat maps indicating locations of hand features. The locations of hand features are used to generate a user hand shape model customized to the size and shape of the user's hand. The user hand shape model is defined by a set of principal component hand shapes defining a hand shape variation space. The limited number of principal component hand shape models reduces determination of user hand shape to a smaller number of variables, and thus provides for a fast calibration of the user hand shape model.