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dc.contributor.authorHwang, Jaepyungen_US
dc.contributor.authorPark, Gangraeen_US
dc.contributor.authorKwon, Taesooen_US
dc.contributor.authorIshii, Shinen_US
dc.contributor.editorHauser, Helwig and Alliez, Pierreen_US
dc.date.accessioned2022-10-11T05:24:54Z
dc.date.available2022-10-11T05:24:54Z
dc.date.issued2022
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.14499
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14499
dc.description.abstractIn this study, we focus on developing a motion synthesis framework that generates a natural transition motion between two different behaviours to interact with a moving object. Specifically, the proposed framework generates the transition motion, bridging from a locomotive behaviour to an object interaction behaviour. And, the transition motion should adapt to the spatio‐temporal variation of the target object in an online manner, so as to naturally connect the behaviours. To solve this issue, we propose a framework that combines a regression model and a transition motion planner. The neural network‐based regression model estimates the reference transition strategy to guide the reference pattern of the transitioning, adapted to the varying situation. The transition motion planner reconstructs the transition motion based on the reference pattern while considering dynamic constraints that avoid the footskate and interaction constraints. The proposed framework is validated to synthesize various transition motions while adapting to the spatio‐temporal variation of the object by using object grasping motion, and athletic motions in soccer.en_US
dc.publisher© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd.en_US
dc.subjectanimation systems
dc.subjectanimation
dc.subjectanimation w/constraints
dc.titleTransition Motion Synthesis for Object Interaction based on Learning Transition Strategiesen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersArticles
dc.description.volume41
dc.description.number6
dc.identifier.doi10.1111/cgf.14499
dc.identifier.pages37-50


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