Handedness Neural Network for VR Controller Detection
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Solution Overview
Problem
Current VR systems require users to manually indicate which hand holds a controller, limiting the ability to switch hands seamlessly and accurately rendering the virtual environment, as they do not inherently detect handedness in real-time.
Innovation Solution
Implementing a handedness neural network that uses image capturing devices and machine learning to determine in real-time which hand is holding the controller, allowing for automatic detection and recognition of hand gestures, thereby enabling seamless hand switching without user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual input is used to indicate which hand holds the controller, then the system can identify handedness, but the user experience is degraded due to inability to switch hands seamlessly
Solution Approach 1:
The patent replaces the manual mechanical input system (user verbally or physically indicating hand preference) with an automated optical detection system using image capturing devices and neural networks to automatically detect which hand is holding the controller, enabling seamless hand switching without manual intervention
Solution Approach 2:
The system performs self-detection of handedness by automatically capturing images, processing them through neural networks, and identifying which hand holds the controller without requiring user assistance or manual input, allowing the system to serve itself in determining controller placement
2Measurement precision
If a neural network is implemented for real-time handedness detection, then automatic detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the complex neural network processing into distinct components: image capture module, preprocessing module, neural network inference module, and handedness determination module. This segmentation allows each component to be optimized independently and distributed across available computational resources
Solution Approach 2:
The patent introduces an intermediary layer of image preprocessing and feature extraction before neural network processing, which simplifies the input data and reduces the computational burden on the neural network while maintaining detection accuracy
3Measurement precision
If additional hardware is added to detect handedness, then detection capability is improved, but system cost increases
Solution Approach 1:
The patent makes the existing image capturing devices (cameras in the VR system) perform multiple functions: capturing the virtual environment, capturing the user's hands and controllers, and providing data for handedness detection. This eliminates the need for dedicated additional sensors or cameras
Solution Approach 2:
The patent uses visual copying through image capture of the real-world hands and controllers, creating digital representations that can be analyzed by the neural network to determine handedness without requiring direct physical sensing of the hands
Data Source
AI summary
In at least one aspect, a method can include generating a respective set of training set of images for each label in a handedness model by: receiving the label at an image capturing device, obtaining a set of captured images by recording a pass-through image of a user placing a target object within an overlay of a bounding area animation, the target object corresponding with the label, and associating the label with each image in the set of captured images. The method includes training, using the training images, the handedness model to provide a correct label for an input image.