Filling gaps in motion capture data: a comparison of three statistical methods

This study proposes the application of three methods from the statistical literature for reconstructing the position of an occluded marker in Motion Capture (MoCap) data. Specifically, we investigate the use of Gaussian Process Regression (GPR), the Synthetic Control Method (SCM), and a Matrix Completion (MC) algorithm. Unlike traditional gap-filling techniques, which rely solely on the occluded marker’s time series, these methods exploit relationships between observed and missing markers to improve reconstruction accuracy. To assess their effectiveness, we apply these methods to a MoCap dataset comprising 3656 frames of a 3D human body performing approximately 17 distinct movements. A nested k-fold cross-validation framework is implemented using a selection of markers covering all major body parts. Performance is evaluated using the Mean Absolute Error (MAE). Our findings indicate that all three methods achieve satisfactory reconstruction accuracy, although computational efficiency varies significantly. The MC algorithm produces the most accurate results while also being the fastest method, whereas SCM exhibits the lowest accuracy. Finally, the methods are applied to real occlusions in the available dataset.