Abstract
Missing marker information is a common problem in Motion Capture (MoCap) systems. Commercial MoCap software provides several methods for reconstructing incomplete marker trajectories; however, these methods still rely on manual intervention. Current alternatives proposed in the literature still present drawbacks that prevent their widespread adoption. The lack of fully automated and universal solutions for gap filling is still a reality. We propose an automatic frame-wise gap filling routine that simultaneously explores restrictions between markers’ distance and markers’ dynamics in a least-squares minimization problem. This algorithm constitutes the main contribution of our work by simultaneously overcoming several limitations of previous methods that include not requiring manual intervention, prior training or training data; not requiring information about the skeleton or a dedicated calibration trial and by being able to reconstruct all gaps, even if these are located in the initial and final frames of a trajectory. We tested our approach in a set of artificially generated gaps, using the full body marker set, and compared the results with three methods available in commercial MoCap software: spline, pattern and rigid body fill. Our method achieved the best overall performance, presenting lower reconstruction errors in all tested conditions.
Highlights
Dimitropoulos, Nikos GrammalidisMotion capture (MoCap) systems are used to digitally track and record human motion, with applications in clinical research, sports biomechanics, rehabilitation medicine, video game development, computer animation and others [1]
We tested our approach in a set of artificially generated gaps and compared the results with three other methods available in Vicon Nexus software: spline interpolation, rigid body fill and pattern fill
The method was framed as an optimization problem that has simultaneously explored the restrictions between markers’ distance and their movement dynamics in a set of empirical principles and equations
Summary
Motion capture (MoCap) systems are used to digitally track and record human motion, with applications in clinical research, sports biomechanics, rehabilitation medicine, video game development, computer animation and others [1]. Optical MoCap systems use multiple cameras to estimate the three-dimensional (3D) position of a set of reflective markers that are strategically placed on the subject, allowing the quantitative analysis of human body kinematics [2,3] and the generation of realistic animations [4]. MoCap cameras and its proprietary software are used to calibrate 3D volume with relation to a fixed 3-axis referential. If a marker is inside the volume and can be captured by two or more cameras, its 3D coordinates will be continuously estimated at a fixed frame rate. The resultant gaps impair data quality and compromise the accuracy of the analysis [5,6,7]
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