Transcript PowerPoint
Automatic Joint Parameter Estimation from Magnetic Motion Capture Data James F. O’Brien, Robert E. Bodenheimer, Gabriel J. Brostow, Jessica K. Hodgins Results Results • No joints Results • Mechanical Linkage Results • After doing the chicken dance… Results • Human system Conclusion • Good system for acquiring human limb lengths – Automatically create graphical characters with correct size – Motion reuse – to match the size of the mocap data to the size of the characters Conclusion • Essentially a process of fitting a parameterized model to a distribution – This model was simple… • Rigid bodies, basic joints, no deformation • It did poorly on human shoulder where joint is complicated – More complicated models could be used • Elastic deformation of limbs, joints lacking a simple center of rotation, variation due to inertial properties 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 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 What must be built? • Hierarchy of limbs – Can we infer what is connected to what? In more detail… • For two trackers i and j, where is the joint between them that preserves limb lengths? 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 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 known • Could be considered a preliminary step to IK computation – Joints are reasonable approximation over entire sequence of motion Outline of Method • For two limbs: parent and child – There is some point(s) in the limbs’ coordinate systems that is always the same • World position of joint j should be the same when measured relative to tracker position of parent and child Method • Problems – Q has more rows than columns (system is overconstrained) – Input motions do not span the space of rotations (system is under-constrained) • Solution – Solve for a least squares solution • Small problems with single-axis joints – Solution (joint center) is arbitrary point on axis – Avoid this by exercising full range of motion and all DOF of joints Determining the Body Hierarchy • Finding optimal hierarchy = finding a minimum spanning tree – Body = node – Joint = edge – Fit error (ei) = edge weight • Select root node t • Minimize ei for all joints in the hierarchy • Residual errors cause joints to move apart during playback – Use inferred join locations and create an articulated model with kinematic joint constraints Results • Simulated human figure – Correct hierarchy, errors less than 10-6 m • Wooden mechanical figure with five ball-andsocket joints – 6 trials (last one markers were moved) – Maximum error was 1.1cm – Correct hierarchy in every case • People – “Exercise” set – “Walk” set Results • Male – Max error 4.1 cm – Average less than 1 cm (upper arms 1.4cm and 2.2cm for left and right arms) • Female – Max error 2.4 cm – Average greater than 1 cm • Problems – Upper leg child of other upper leg instead of pelvis • Speed on SGI O2 with 195MHz R10k – < 4 seconds for 45 seconds of motion capture when hierarchy specified – < 14 seconds when hierarchy was not specified Conclusions • Results are good, given resolution of sensors • Provides a fast way to accomplish calibration for magnetic motion capture systems • Applications where explicit calibration is infeasible – “Exercise” could be a pre-show portion of location-based entertainment experience