3D Model Orientation Initialization for Markerless Gesture Control
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Solution Overview
Problem
Conventional motion capture systems rely on markers or sensors that are cumbersome and expensive, and require complex equipment, limiting their deployment and real-time capability.
Innovation Solution
A method for initializing the orientation of a three-dimensional model of an object in space using observed information, which detects contours, calculates a representative normal, and applies a primary orientation parameter to align the model, enabling real-time control and interaction without the need for physical contact or bulky equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If markers or sensors are worn by the subject to capture motion, then measurement precision is improved, but ease of operation deteriorates due to cumbersome equipment interfering with natural movement
Solution Approach 1:
The patent extracts and removes the markers and sensors from the system, transitioning from instrumented motion capture to markerless optical flow analysis. This extraction eliminates the interference with natural movement while maintaining motion capture capability through direct analysis of visual information from video sequences.
Solution Approach 2:
The patent replaces the mechanical system of physical markers and sensors with an optical field-based system. Instead of attaching physical objects to the subject, the system uses computer vision algorithms to analyze optical flow in video sequences, substituting mechanical measurement with optical field analysis.
2Measurement precision
If numerous cameras are deployed to capture subject movements, then measurement precision is improved, but device complexity increases due to the volume of data requiring analysis
Solution Approach 1:
The patent merges the functionality of multiple cameras into a unified optical flow analysis framework. Instead of processing data from numerous separate camera systems independently, the invention integrates their observations into a cohesive model through optical flow field analysis, reducing overall system complexity while maintaining measurement precision.
Solution Approach 2:
The patent introduces optical flow field analysis as an intermediary between raw camera data and motion capture results. This intermediary layer processes and synthesizes information from multiple camera views in a unified manner, simplifying the data analysis pipeline compared to direct processing of individual camera feeds.
3Measurement precision
If conventional motion capture systems are used, then measurement precision is improved, but productivity deteriorates due to lack of real-time capability
Solution Approach 1:
The patent performs preliminary computation by pre-calculating and storing optical flow field characteristics and motion capture model parameters. This preliminary action enables real-time processing during actual use, as the computationally intensive analysis has already been performed and the results are ready for immediate application.
Solution Approach 2:
The patent implements a dynamic system that adapts the level of processing based on real-time requirements. The optical flow analysis and motion capture model can operate at different computational depths, allowing the system to maintain measurement precision while adjusting processing speed to meet real-time productivity demands.
Data Source
AI summary
The technology disclosed relates to initializing orientation of a three-dimensional (3D) model of an object. In particular, it relates to accessing at least one three-dimensional (3D) model of an object and observed information of the object movable in space and determining a primary orientation parameter of the model from the observed information. The method further includes detecting contours of the object in the observed information and calculating a representative normal to the detected contours, accessing a vector representing a 3D angle from the object to a point of observation, calculating a primary orientation of the object as a cross-product of the representative normal and the vector, and using the calculated primary orientation parameter to initialize the model.