Momentum-Mapped Inverted Pendulum Models for Controlling Dynamic Human Motions
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kwon, Taesoo | - |
dc.contributor.author | Hodgins, Jessica K. | - |
dc.date.accessioned | 2022-07-14T17:02:01Z | - |
dc.date.available | 2022-07-14T17:02:01Z | - |
dc.date.created | 2021-05-12 | - |
dc.date.issued | 2017-02 | - |
dc.identifier.issn | 0730-0301 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/152965 | - |
dc.description.abstract | Designing a unified framework for simulating a broad variety of human behaviors has proven to be challenging. In this article, we present an approach for control system design that can generate animations of a diverse set of behaviors including walking, running, and a variety of gymnastic behaviors. We achieve this generalization with a balancing strategy that relies on a new form of inverted pendulum model (IPM), which we call the momentummapped IPM (MMIPM). We analyze reference motion capture data in a pre-processing step to extract the motion of the MMIPM. To compute a new motion, the controller plans a desired motion, frame by frame, based on the current pendulum state and a predicted pendulum trajectory. By tracking this time-varying trajectory, the controller creates a character that dynamically balances, changes speed, makes turns, jumps, and performs gymnastic maneuvers. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ASSOC COMPUTING MACHINERY | - |
dc.title | Momentum-Mapped Inverted Pendulum Models for Controlling Dynamic Human Motions | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kwon, Taesoo | - |
dc.identifier.doi | 10.1145/2983616 | - |
dc.identifier.scopusid | 2-s2.0-85010824720 | - |
dc.identifier.wosid | 000395473300001 | - |
dc.identifier.bibliographicCitation | ACM TRANSACTIONS ON GRAPHICS, v.36, no.1, pp.1 - 14 | - |
dc.relation.isPartOf | ACM TRANSACTIONS ON GRAPHICS | - |
dc.citation.title | ACM TRANSACTIONS ON GRAPHICS | - |
dc.citation.volume | 36 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 14 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.subject.keywordPlus | Balancing strategy | - |
dc.subject.keywordPlus | Character animation | - |
dc.subject.keywordPlus | Inverted pendulum model | - |
dc.subject.keywordPlus | Motion capture | - |
dc.subject.keywordPlus | Motion capture data | - |
dc.subject.keywordPlus | Pre-processing step | - |
dc.subject.keywordPlus | Rigid-body simulations | - |
dc.subject.keywordPlus | Time-varying trajectories | - |
dc.subject.keywordAuthor | Experimentation | - |
dc.subject.keywordAuthor | Human Factors | - |
dc.subject.keywordAuthor | Character animation | - |
dc.subject.keywordAuthor | motion capture and reuse | - |
dc.subject.keywordAuthor | rigid body simulation | - |
dc.identifier.url | https://dl.acm.org/doi/10.1145/2983616 | - |
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