Style Invariant Locomotion Classification for Character Control

dc.contributor.authorBoehs, G.E.en_US
dc.contributor.authorVieira, M.L.H.en_US
dc.contributor.editorChen, Min and Benes, Bedrichen_US
dc.date.accessioned2019-03-17T09:57:02Z
dc.date.available2019-03-17T09:57:02Z
dc.date.issued2019
dc.description.abstractWe present a real‐time system for character control that relies on the classification of locomotive actions in skeletal motion capture data. Our method is both progress dependent and style invariant. Two deep neural networks are used to correlate body shape and implicit dynamics to locomotive types and their respective progress. In comparison to related work, our approach does not require a setup step and enables the user to act in a natural, unconstrained manner. Also, our method displays better performance than the related work in scenarios where the actor performs sharp changes in direction and highly stylized motions while maintaining at least as good performance in other scenarios. Our motivation is to enable character control of non‐bipedal characters in virtual production and live immersive experiences, where mannerisms in the actor's performance may be an issue for previous methods.We present a real‐time system for character control that relies on the classification of locomotive actions in skeletal motion capture data. Our method is both progress dependent and style invariant. Two deep neural networks are used to correlate body shape and implicit dynamics to locomotive types and their respective progress. In comparison to related work, our approach does not require a setup step and enables the user to act in a natural, unconstrained manner. Also, our method displays better performance than the related work in scenarios where the actor performs sharp changes in direction and highly stylized motions while maintaining at least as good performance in other scenarios. Our motivation is to enable character control of non‐bipedal characters in virtual production and live immersive experiences, where mannerisms in the actor's performance may be an issue for previous methods.en_US
dc.description.number1
dc.description.sectionheadersArticles
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume38
dc.identifier.doi10.1111/cgf.13590
dc.identifier.issn1467-8659
dc.identifier.pages537-548
dc.identifier.urihttps://doi.org/10.1111/cgf.13590
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13590
dc.publisher© 2019 The Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectmotion capture
dc.subjectanimation
dc.subjectmotion control
dc.subject• Computing methodologies → Computer vision ‐ Motion capture; Animation ‐ Motion capture
dc.titleStyle Invariant Locomotion Classification for Character Controlen_US
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