jointEstimation

jointEstimation - Automatic Joint Parameter Estimation from...

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Automatic Joint Parameter Estimation from Magnetic Motion Capture Data James F. O’Brien, Robert E. Bodenheimer, Gabriel J. Brostow, Jessica K. Hodgins Presented by Jeff Stricker
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Why is Motion Capture Good? Human and human-like characters Retains subtle elements of a performer’s style Powerful solution when animations must be generated quickly Immersive virtual environments “because you squished a jellyfish and took out the magic juice”
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Why is Motion Capture Bad? Optical and magnetic systems suffer from sensor noise Sensors require careful calibration Precise measurements of limb lengths Taken by hand in a reference pose Tedious Prone to error Often impractical “this has been my life’s passion for the past day”
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Purpose of Algorithm Address the problems associated with calibration by automatically computing joint locations Perks No external measurement No constraints on sensor placement No posing in particular configurations Requirement Data must exercise all DOF of joints for an unambiguous answer “minification is used to create a minified version”
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Method Comparison Some methods Joint location determined from weighted average of points Concerned with detailed model from data points Use multiple markers for redundancy Instantaneous joint center from single instant of motion This method Does not require manual processing of data More concerned with creating animation Not an IK technique since dimensions of skeleton aren’t
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jointEstimation - Automatic Joint Parameter Estimation from...

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