Gesture-Based Virtual Content Casting in AR Near-Eye Displays
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Solution Overview
Problem
Current user interaction methods in augmented reality systems are not intuitive and do not fully exploit the technical capabilities of these systems, making them suboptimal for user experience and interaction.
Innovation Solution
A near eye display system that uses gesture recognition to allow users to interact with virtual content by simulating actions such as throwing or pinching virtual objects, enabling intuitive manipulation and casting of content items within an artificial reality environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If physical interaction with devices is used to enable changes, then the interaction method is simple and reliable, but it is not intuitive for augmented reality users and does not exploit AR system capabilities
Solution Approach 1:
The patent replaces physical mechanical interaction with gesture-based interaction. Users perform gestures in the air (e.g., pinching, throwing motions) to manipulate virtual content and control devices, eliminating the need for direct physical contact with device buttons or interfaces. This substitution maintains simplicity while dramatically improving intuitiveness for AR users.
Solution Approach 2:
The patent introduces gesture recognition technology as an intermediary between the user and the device. The system captures hand movements via imaging devices, interprets them as specific gestures, and translates them into device commands. This intermediary layer enables natural, intuitive interaction without requiring users to physically touch or navigate device interfaces.
2Adaptability or versatility
If gesture recognition is implemented for virtual content manipulation, then user experience is enhanced and AR capabilities are exploited, but the system complexity increases
Solution Approach 1:
The patent implements a universal gesture recognition system that can identify and respond to multiple different gestures (pinching, throwing, pointing, etc.) using a single integrated approach. The same imaging devices and processing algorithms handle various gesture types, enabling versatile interaction without requiring separate specialized systems for each gesture type.
Solution Approach 2:
The patent employs dynamic gesture recognition that adapts to different interaction contexts. The system can distinguish between different phases of gestures (e.g., wind-up phase, release phase) and interpret them accordingly. This dynamic interpretation allows the system to handle versatile interaction scenarios while maintaining a relatively simple underlying architecture.
3Measurement precision
If multiple hand positions are tracked to identify gesture portions, then gesture accuracy is improved, but the processing complexity and time increase
Solution Approach 1:
The patent performs preliminary processing of hand position data by continuously tracking and storing hand positions at multiple time points. This pre-processing creates a ready-to-analyze dataset of hand positions, so when a gesture is detected, the system can quickly identify gesture portions (wind-up, release phases) without performing extensive real-time calculations, thus reducing processing time while maintaining high precision.
Data Source
AI summary
Embodiments are directed to a near eye display (NED) system for displaying artificial reality content to a user and to manipulate displayed content items based upon gestures performed by users of the NED system. A user of the NED system may perform a gesture simulating the throwing of an object to “cast” a content item to a target location in an artificial reality (AR) environment displayed by the NED system. The gesture may comprise a first portion in which the user's hand “grabs” or “pinches” a virtual object corresponding to the content item and moves backwards relative to their body, and a second portion in which the user's hand moves forwards relative to their body and releases the virtual object. The target location may be identified based upon a trajectory associated with the backwards motion of the first portion of the gesture.