Real-Time Planning for Parameterized Human Motion

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dc.contributor.authorLo, Wan-Yenen_US
dc.contributor.authorZwicker, Matthiasen_US
dc.contributor.editorMarkus Gross and Doug Jamesen_US
dc.date.accessioned2014-01-29T07:36:58Z
dc.date.available2014-01-29T07:36:58Z
dc.date.issued2008en_US
dc.description.abstractWe present a novel approach to learn motion controllers for real-time character animation based on motion capture data. We employ a tree-based regression algorithm for reinforcement learning, which enables us to generate motions that require planning. This approach is more flexible and more robust than previous strategies. We also extend the learning framework to include parameterized motions and interpolation. This enables us to control the character more precisely with a small amount of motion data. Finally, we present results of our algorithm for three different types of controllers.en_US
dc.description.seriesinformationEurographics/SIGGRAPH Symposium on Computer Animationen_US
dc.identifier.isbn978-3-905674-10-1en_US
dc.identifier.issn1727-5288en_US
dc.identifier.urihttps://doi.org/10.2312/SCA/SCA08/029-038en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Animationen_US
dc.titleReal-Time Planning for Parameterized Human Motionen_US
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SCA 08: Eurographics/SIGGRAPH Symposium on Computer Animation