Dynamic Energy Field for Nuanced Virtual Object Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Natural user-input technologies often lack granularity and nuance in controlling virtual environments, as they do not account for variations in motion when performing discrete gestures, and motions that do not conform to specific gestures may not produce the expected results.
Innovation Solution
The use of optical flow analysis to generate a dynamic energy field that influences properties of virtual environments, allowing for more responsive, granular, and nuanced control by mapping motion values from human subjects into energy elements that interact with virtual objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If discrete gesture recognition is used to control virtual environments, then gesture-based interaction is achieved, but control granularity and responsiveness to motion variations are lost
Solution Approach 1:
The patent segments the continuous motion data into discrete gesture events by identifying start and end points of gestures, then analyzes the motion characteristics within each segment. This allows the system to maintain gesture-based interaction while capturing motion variations through temporal and spatial analysis of segmented motion data.
Solution Approach 2:
The patent implements dynamic threshold adjustment based on motion velocity and acceleration patterns. The system adapts gesture recognition parameters in real-time based on the speed and characteristics of motion, enabling both robust gesture detection and sensitive motion variation detection within the same framework.
2Measurement precision
If motion thresholds are set to be highly sensitive to detect all motions, then motion detection precision improves, but false detection of non-gesture motions increases
Solution Approach 1:
The patent employs feedback mechanisms where the system continuously monitors motion patterns and adjusts detection thresholds based on learned characteristics of valid gestures versus spurious motions. The system uses historical motion data to refine threshold settings, reducing false detections while maintaining sensitivity to genuine gestures.
Solution Approach 2:
The patent dynamically changes multiple parameters including velocity thresholds, acceleration thresholds, and temporal window sizes based on the current motion context. By adjusting these parameters in response to detected motion patterns, the system maintains high detection precision while filtering out false positives through contextual parameter adaptation.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
An optical flow of depth video of a depth camera imaging a human subject is recognized. An energy field created by motion of the human subject is generated as a function of the optical flow and specified rules of a physical simulation of the virtual environment. The energy field is mapped to a virtual position in the virtual environment. A property of a virtual object in the virtual environment is adjusted based on a plurality of energy elements of the energy field in response to the virtual object interacting with the virtual position of the energy field.