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dc.contributor.authorNoshaba, Cheemaen_US
dc.contributor.authorHosseini, Somayehen_US
dc.contributor.authorSprenger, Janisen_US
dc.contributor.authorHerrmann, Eriken_US
dc.contributor.authorDu, Hanen_US
dc.contributor.authorFischer, Klausen_US
dc.contributor.authorSlusallek, Philippen_US
dc.contributor.editorSkouras, Melinaen_US
dc.date.accessioned2018-07-10T18:00:58Z
dc.date.available2018-07-10T18:00:58Z
dc.date.issued2018
dc.identifier.isbn978-3-03868-070-3
dc.identifier.issn1727-5288
dc.identifier.urihttps://doi.org/10.2312/sca.20181185
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/sca20181185
dc.description.abstractSemantic 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.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectMotion processing
dc.subjectMotion capture
dc.subjectImage processing
dc.titleDilated Temporal Fully-Convolutional Network for Semantic Segmentation of Motion Capture Dataen_US
dc.description.seriesinformationEurographics/ ACM SIGGRAPH Symposium on Computer Animation - Posters
dc.description.sectionheadersPosters
dc.identifier.doi10.2312/sca.20181185
dc.identifier.pages5-6


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