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Optical motion capture systems have become a widely used technology invarious fields, such as augmented reality, robotics, movie production, etc.Such systems use a large number of cameras to triangulate the position ofoptical markers.The marker positions are estimated with high accuracy. However,especially when tracking articulated bodies, a fraction of the markers in eachtimestep is missing from the reconstruction. In this paper, we propose to use aneural network approach to learn how human motion is temporally and spatiallycorrelated, and reconstruct missing markers positions through this model. Weexperiment with two different models, one LSTM-based and one time-window-based.Both methods produce state-of-the-art results, while working online, as opposedto most of the alternative methods, which require the complete sequence to beknown. The implementation is publicly available athttps://github.com/Svito-zar/NN-for-Missing-Marker-Reconstruction .

A Neural Network Approach to Missing Marker Reconstruction in Human Motion Capture