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arXiv · 1806.09174

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

Noshaba Cheema·Somayeh Hosseini·Janis Sprenger·Erik Herrmann·Han Du·Klaus Fischer·Philipp Slusallek
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Abstract

Semantic segmentation of motion capture sequences plays a key part in many data-driven motion synthesis frameworks. It is a preprocessing step in which long recordings of motion capture sequences are partitioned into smaller segments. Afterwards, additional methods like statistical modeling can be applied to each group of structurally-similar segments to learn an abstract motion 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 motion capture data using a dilated temporal fully-convolutional network. Our model outperforms a state-of-the-art model in action segmentation, as well as three networks for sequence modeling. We further show our model is robust against high noisy training labels.

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Noshaba Cheema, Somayeh Hosseini, Janis Sprenger, Erik Herrmann, Han Du, Klaus Fischer, Philipp Slusallek. 2018-06-24. Dilated Temporal Fully-Convolutional Network for Semantic Segmentation of Motion Capture Data. https://doi.org/10.2312/sca.20181185

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