A Neural Network Approach to Missing Marker Reconstruction
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Optical motion capture systems have become a widely used technology in various fields, such as augmented reality, robotics, movie production, etc. Such systems use a large number of cameras to triangulate the position of optical markers. These are then used to reconstruct the motion of rigid objects or human articulated bodies, to which the markers are attached. The marker positions are estimated with high accuracy. However, especially when tracking articulated bodies, a fraction of the markers in each timestep is missing from the reconstruction. In this paper, we propose to use a neural network approach to learn how human motion is temporally and spatially correlated, and reconstruct missing markers positions through this model. We experiment with two different models, one LSTM-based and one window-based. Experiments on the CMU Mocap dataset show that we outperform the state of the art by 20% - 400%.
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