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Semantic segmentation of motion capture sequences plays a key part in manydata-driven motion synthesis frameworks. It is a preprocessing step in whichlong recordings of motion capture sequences are partitioned into smallersegments. Afterwards, additional methods like statistical modeling can beapplied to each group of structurally-similar segments to learn an abstractmotion manifold. The segmentation task however often remains a manual task,which increases the effort and cost of generating large-scale motion databases.We therefore propose an automatic framework for semantic segmentation of motioncapture data using a dilated temporal fully-convolutional network. Our modeloutperforms a state-of-the-art model in action segmentation, as well as threenetworks for sequence modeling. We further show our model is robust againsthigh noisy training labels.

Dilated Temporal Fully-Convolutional Network for Semantic Segmentation of Motion Capture Data