VR Head Tracking via Landscape Topology
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Solution Overview
Problem
Conventional approaches to head tracking in virtual reality content presentation require numerous calculations, degrading user experience due to the need for evaluating vast amounts of sensor data, and are not effective in accurately predicting user attention within immersive video streams.
Innovation Solution
The use of physics-based models, such as landscape and celestial representations, to predict user focal points by simulating particle movement based on heat map data and audio-based points of interest, optimizing resource usage and improving prediction speed and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sensor data evaluation methods are used for head tracking, then comprehensive user attention data can be collected, but calculation complexity increases and prediction speed decreases
Solution Approach 1:
The patent extracts only the essential features from vast sensor data to create simplified landscape and celestial representations. Instead of processing all sensor data, the system identifies and extracts key visual and audio cues that drive user attention, converting complex sensor inputs into simplified topological maps that retain predictive power while reducing computational burden.
Solution Approach 2:
The patent transforms sensor data from its original complex form into simplified topological parameters through landscape and celestial representations. By changing the parameter space from raw sensor values to topological features (peaks, valleys, gravitational fields), the system achieves faster processing while maintaining prediction accuracy.
2Measurement precision
If vast amounts of sensor data are evaluated for head tracking, then prediction accuracy may improve, but processing time increases and user experience degrades
Solution Approach 1:
The patent performs preliminary action by pre-processing sensor data into landscape and celestial representations before actual prediction is needed. These pre-computed topological structures enable rapid query-time predictions without re-processing raw sensor data, significantly reducing real-time processing delays.
Solution Approach 2:
The patent creates simplified copies of the complex sensor data environment through landscape and celestial representations. These topological copies capture the essential structure of user attention patterns without containing all the detail of original sensor data, enabling fast predictions through operations on the simplified models.
3Measurement precision
If comprehensive sensor data processing is performed, then head tracking accuracy improves, but resource consumption increases
Solution Approach 1:
The system extracts only the critical features needed for prediction from comprehensive sensor data, discarding redundant information. By taking out only the essential visual and audio cues that drive attention, the system maintains accuracy while dramatically reducing computational resource requirements.
Solution Approach 2:
The patent changes parameters from high-dimensional sensor readings to low-dimensional topological representations. This parameter transformation reduces computational complexity and resource consumption while preserving the predictive information necessary for accurate head tracking.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can determine a first position corresponding to a user focal point prior to presenting a given frame of a content item. A landscape representation of the given frame is determined. The landscape representation describes the respective popularity of one or more regions in the frame as a topology. A second position corresponding to the user focal point is determined based at least in part on the landscape representation and the first position. The second position is predicted to be the position of the user focal point when presenting the given frame.