VR Pose Tracking via Multi-Device Data Fusion
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Solution Overview
Problem
Current virtual reality (VR) systems face challenges in body pose tracking due to the inability of tracking devices to communicate and integrate effectively, leading to errors when the human body is occluded or the field of view is poor.
Innovation Solution
A virtual reality system that includes a head-mounted display device and multiple tracking devices, each equipped with a camera and processor, which communicate to obtain and process images of the human body. The system predicts the 3D pose of the body, determines valid values based on previous poses and confidence levels, and outputs the optimized pose to a main tracking device for display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple tracking devices are used to track human body pose, then tracking coverage is improved, but the ability to communicate and integrate data between devices is insufficient
Solution Approach 1:
The patent combines data from multiple tracking devices by establishing a main tracking device that receives and integrates pose data from auxiliary tracking devices. The system merges prediction results from multiple sources through data fusion techniques, including weighted fusion and covariance intersection, to generate a comprehensive and accurate human body pose estimate that leverages the combined coverage of all devices.
Solution Approach 2:
The patent creates a universal data integration framework that can handle data from different types of tracking devices with varying confidence levels and data qualities. The system uses a unified prediction model that processes inputs from multiple devices universally, adapting to different device characteristics while maintaining consistent integration methodology across the entire tracking system.
2Device complexity
If tracking devices operate independently, then device complexity is reduced, but prediction accuracy deteriorates when human body is occluded or field of view is poor
Solution Approach 1:
The patent implements feedback mechanisms where each tracking device sends its prediction results and confidence levels to the main tracking device. The system uses this feedback to dynamically adjust weighting factors and fusion strategies, allowing the main device to compensate for poor observations from individual devices. When occlusion or poor field of view is detected, the feedback loop enables the system to rely more heavily on predictions from devices with better observations.
Solution Approach 2:
The patent creates a composite prediction system that combines results from multiple independent tracking devices into a unified pose estimate. Rather than relying on a single device, the system synthesizes predictions from multiple sources with different observation qualities, creating a more robust and accurate final result that leverages the strengths of each independent device while mitigating their individual weaknesses.
3Device complexity
If simple tracking devices are used, then device cost and complexity are reduced, but the ability to handle occlusion and poor field of view is insufficient
Solution Approach 1:
The patent divides the tracking function into segments distributed across multiple simple tracking devices, each responsible for observing specific portions of the human body from different angles. Rather than requiring one complex device to handle all scenarios, the system segments the observation task among multiple simpler devices, where each device can independently track visible body parts while the main device integrates these segmented observations into a complete pose estimate.
Data Source
AI summary
A virtual reality system includes a head-mounted display device and several tracking devices is disclosed. Each tracking devices includes a camera and a processor. The camera obtains a picture of a human body of a current time point. The processor is configured to: obtain a current predicted 3D pose and a confidence of the current time point according to the picture; determine a previous valid value according to a previous predicted 3D pose and a previous final optimized pose; determine a current valid value according to the previous valid value, the confidence, and the current predicted 3D pose; and output the current predicted 3D pose and the confidence to a main tracking device of the tracking devices according to the current valid value, so as to generate a current final optimized pose.