Autonomous real-time model building for optical motion capture

We present a robust framework for real-time processing of 3D motion capture data. It autonomously analyzes the input data to build a model of the observed subjects, and is expected to perform in situations where the observed features are unstable or frequently occluded. We have implemented the method and presently rely on a marker based motion capture system to provide unlabeled 3D coordinates of non-occluded markers. However, we hope this to be a step towards using regular video camera systems with feature detection to perform autonomous markerless motion capture.