VR Gesture Navigation for Precise 3D Position and Rotation Control
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Solution Overview
Problem
Existing virtual reality systems face limitations in user movement and rotation within VR environments, leading to reduced accuracy, precision, and responsiveness, making interactions cumbersome and inefficient.
Innovation Solution
A virtual reality system that utilizes hand gestures to manipulate user movement and rotation within a 3D coordinate system, generating vectors based on detected gestures to adjust user coordinates and modify image data accordingly, allowing intuitive control without physical controllers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional VR control methods are used, then basic movement is achieved, but user movement accuracy and precision deteriorate
Solution Approach 1:
The patent replaces traditional mechanical controller-based navigation with a gesture recognition system that uses computer vision and machine learning to detect and interpret user hand movements. This substitution enables more precise and intuitive control by directly mapping natural gestures to virtual environment navigation commands, improving both accuracy and ease of operation.
Solution Approach 2:
The system dynamically adjusts movement parameters such as speed, acceleration, and rotation rates based on the detected gesture type and intensity. By changing these parameters in response to user input, the system achieves both high precision for fine movements and responsiveness for larger actions, resolving the contradiction between accuracy and ease of operation.
2Measurement precision
If physical controllers are required for navigation, then control precision is maintained, but device complexity and user fatigue increase
Solution Approach 1:
The patent extracts the navigation control function from physical controllers and implements it through gesture recognition in the virtual environment itself. By removing the need for external controllers and using only the user's hands as input devices, the system reduces device complexity while maintaining control precision through sophisticated gesture detection algorithms.
Solution Approach 2:
The system uses the user's own body (hands and arms) as the control interface rather than requiring separate controllers. This self-service approach eliminates additional hardware while maintaining precise control through the natural dexterity and range of motion of human hands, reducing both device complexity and user fatigue.
3Productivity
If traditional movement methods are used, then system simplicity is maintained, but user interaction efficiency deteriorates
Solution Approach 1:
The patent implements dynamic gesture recognition that adapts to different interaction contexts and user preferences. The system can switch between different gesture interpretations and movement modes based on the current task, improving interaction efficiency by providing the most appropriate control method for each situation while maintaining ease of operation through natural gestures.
Solution Approach 2:
The system adds gesture recognition as a new dimension of interaction beyond traditional controller buttons and joysticks. By incorporating hand pose detection and gesture classification, the patent creates a multi-dimensional control interface that significantly improves interaction efficiency for complex tasks while maintaining simplicity for basic operations.
Data Source
AI summary
A method includes using at least one processor to display image data to a user via an electronic device based on a three-dimensional (3D) user coordinate in a 3D coordinate system. Further, the method includes detecting a first gesture associated with an extremity of the user and a first 3D coordinate of the 3D coordinate system, detecting a movement of the extremity in the 3D coordinate system, from the first 3D coordinate to a second 3D coordinate of the 3D coordinate system, and generating a vector based on the movement, a distance between the first 3D coordinate and the second 3D coordinate, and direction of the movement. The method also includes adjusting the 3D user coordinate based on the vector and modifying the image data based on the adjusted 3D user coordinate.