Herrmann, ErikDu, HanAntakli, AndréRubinstein, DmitriSchubotz, RenéSprenger, JanisHosseini, SomayehCheema, NoshabaZinnikus, IngoManns, MartinFischer, KlausSlusallek, PhilippAgus, Marco and Corsini, Massimiliano and Pintus, Ruggero2019-11-202019-11-202019978-3-03868-100-72617-4855https://doi.org/10.2312/stag.20191366https://diglib.eg.org:443/handle/10.2312/stag20191366Machine 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.Computing methodologiesMotion captureMotion processingHumancentered computingVisualization toolkits10.2312/stag.2019136679-88