Motion Data and Model Management for Applied Statistical Motion Synthesis

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dc.contributor.authorHerrmann, Eriken_US
dc.contributor.authorDu, Hanen_US
dc.contributor.authorAntakli, Andréen_US
dc.contributor.authorRubinstein, Dmitrien_US
dc.contributor.authorSchubotz, Renéen_US
dc.contributor.authorSprenger, Janisen_US
dc.contributor.authorHosseini, Somayehen_US
dc.contributor.authorCheema, Noshabaen_US
dc.contributor.authorZinnikus, Ingoen_US
dc.contributor.authorManns, Martinen_US
dc.contributor.authorFischer, Klausen_US
dc.contributor.authorSlusallek, Philippen_US
dc.contributor.editorAgus, Marco and Corsini, Massimiliano and Pintus, Ruggeroen_US
dc.date.accessioned2019-11-20T08:12:41Z
dc.date.available2019-11-20T08:12:41Z
dc.date.issued2019
dc.description.abstractMachine learning based motion modelling methods such as statistical modelling require a large amount of input data. In practice, the management of the data can become a problem in itself for artists who want to control the quality of the motion models. As a solution to this problem, we present a motion data and model management system and integrate it with a statistical motion modelling pipeline. The system is based on a data storage server with a REST interface that enables the efficient storage of different versions of motion data and models. The database system is combined with a motion preprocessing tool that provides functions for batch editing, retargeting and annotation of the data. For the application of the motion models in a game engine, the framework provides a stateful motion synthesis server that can load the models directly from the data storage server. Additionally, the framework makes use of a Kubernetes compute cluster to execute time consuming processes such as the preprocessing and modelling of the data. The system is evaluated in a use case for the simulation of manual assembly workers.en_US
dc.description.sectionheadersFull Papers
dc.description.seriesinformationSmart Tools and Apps for Graphics - Eurographics Italian Chapter Conference
dc.identifier.doi10.2312/stag.20191366
dc.identifier.isbn978-3-03868-100-7
dc.identifier.issn2617-4855
dc.identifier.pages79-88
dc.identifier.urihttps://doi.org/10.2312/stag.20191366
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/stag20191366
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectMotion capture
dc.subjectMotion processing
dc.subjectHuman
dc.subjectcentered computing
dc.subjectVisualization toolkits
dc.titleMotion Data and Model Management for Applied Statistical Motion Synthesisen_US
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