Motion Data and Model Management for Applied Statistical Motion Synthesis

Abstract
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.
Description

        
@inproceedings{
10.2312:stag.20191366
, booktitle = {
Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference
}, editor = {
Agus, Marco and Corsini, Massimiliano and Pintus, Ruggero
}, title = {{
Motion Data and Model Management for Applied Statistical Motion Synthesis
}}, author = {
Herrmann, Erik
 and
Du, Han
 and
Fischer, Klaus
 and
Slusallek, Philipp
 and
Antakli, André
 and
Rubinstein, Dmitri
 and
Schubotz, René
 and
Sprenger, Janis
 and
Hosseini, Somayeh
 and
Cheema, Noshaba
 and
Zinnikus, Ingo
 and
Manns, Martin
}, year = {
2019
}, publisher = {
The Eurographics Association
}, ISSN = {
2617-4855
}, ISBN = {
978-3-03868-100-7
}, DOI = {
10.2312/stag.20191366
} }
Citation