Presenting a Deep Motion Blending Approach for Simulating Natural Reach Motions

dc.contributor.authorGaisbauer, Felixen_US
dc.contributor.authorFroehlich, Philippen_US
dc.contributor.authorLehwald, Jannesen_US
dc.contributor.authorAgethen, Philippen_US
dc.contributor.authorRukzio, Enricoen_US
dc.contributor.editorJain, Eakta and Kosinka, Jiríen_US
dc.date.accessioned2018-04-14T18:29:51Z
dc.date.available2018-04-14T18:29:51Z
dc.date.issued2018
dc.description.abstractMotion blending and character animation systems are widely used in different domains such as gaming or simulation within production industries. Most of the established approaches are based on motion blending techniques. These approaches provide natural motions within common scenarios while inducing low computational costs. However, with increasing amount of influence parameters and constraints such as collision-avoidance, they increasingly fail or require a vast amount of time to meet these requirements. With ongoing progress in artificial intelligence and neural networks, recent works present deep learning based approaches for motion synthesis, which offer great potential for modeling natural motions, while considering heterogeneous influence factors. In this paper, we propose a novel deep blending approach to simulate non-cyclical natural reach motions based on an extension of phase functioned deep neural networks.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationEG 2018 - Posters
dc.identifier.doi10.2312/egp.20181010
dc.identifier.issn1017-4656
dc.identifier.pages5-6
dc.identifier.urihttps://doi.org/10.2312/egp.20181010
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/egp20181010
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectCollision detection
dc.subjectSimulation types and techniques
dc.subjectAnimation
dc.titlePresenting a Deep Motion Blending Approach for Simulating Natural Reach Motionsen_US
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