Dynamic XR Content Placement via Environmental Characterization
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Solution Overview
Problem
Existing content placement in virtual or extended reality environments is often static and manually determined, lacking an informed approach based on contextual metadata, which limits the dynamic and adaptive placement of XR content.
Innovation Solution
A method and system for 'smart' content placement that involves obtaining a representation of an environment, determining candidate content placement locations, characterizing these locations with metadata, and selecting XR content that matches these parameters for dynamic and informed placement within the environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If content placement is done manually and statically, then placement simplicity is maintained, but adaptability and contextual relevance deteriorate
Solution Approach 1:
The system performs preliminary parsing of the XR environment to identify candidate content placement locations and determine their characterization parameters before actual content placement. This advance preparation enables adaptive content selection based on environmental characteristics without adding complexity to the real-time placement operation.
Solution Approach 2:
The system automatically analyzes the XR environment, identifies suitable placement locations, and matches content based on characterization parameters without requiring manual intervention. The environment itself provides the information needed for intelligent placement through automated parsing and characterization.
2Extent of automation
If automated content placement is implemented, then adaptability improves, but system complexity increases
Solution Approach 1:
The automated placement system is divided into distinct functional modules: environment parsing to identify candidate locations, characterization parameter determination for each location, and content selection based on parameter matching. This segmentation manages complexity by organizing automation into manageable, independent components.
Solution Approach 2:
The system performs preliminary automated analysis of the XR environment to generate a structured understanding of candidate placement locations and their characteristics before content selection occurs. This advance processing enables high-level automation without overwhelming system complexity during the actual placement operation.
3Manufacturing precision
If characterization parameters are determined for candidate locations, then content placement precision improves, but processing time increases
Solution Approach 1:
The system determines characterization parameters for all candidate content placement locations in advance, before actual content selection and placement occurs. This preliminary characterization enables rapid content matching and placement execution, reducing the time required during the actual content delivery phase.
Solution Approach 2:
The system dynamically adjusts the level of characterization detail based on the specific XR environment and content requirements. By making the characterization process adaptive rather than static, the system optimizes the balance between placement precision and processing time according to actual needs.
Data Source
AI summary
In some implementations, a method includes: obtaining a representation an environment; determining a plurality of candidate content placement locations within the environment based on the representation of the environment; determining characterization parameters for the plurality of candidate content placement locations; obtaining extended reality (XR) content selected based on a match between content parameters associated with the XR content and characterization parameters for a respective candidate content placement location among the plurality of candidate content placement locations; and displaying, via the display device, the XR content at the respective candidate content placement location within the environment.