Motion Data and Model Management for Applied Statistical Motion Synthesis

Erik Herrmann, Han Du, André Antakli, Dmitri Rubinstein, René Schubotz, Janis Sprenger, Somayeh Hosseini, Noshaba Cheema, Ingo Zinnikus, Martin Manns, Klaus Fischer, Philipp Slusallek

In: Marco Agus, Massimiliano Corsini, Ruggero Pintus (Hrsg.). Smart Tools and Applications in Computer Graphics 2019. Smart Tools and Applications in Computer Graphics (STAG-2019) November 14-15 Cagliari Sardinia Italy Seiten 79-88 ISBN 978-3-03868-100-7 The Eurographics Association 11/2019.


Machine 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.


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STAG_2019_paper_13_-_2019-11-18T130714.171.pdf (pdf, 2 MB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence