Modularized Predictive Coding-Based Online Motion Synthesis Combining Environmental Constraints and Motion-Capture Data
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hwang, Jaepyung | - |
dc.contributor.author | Ishii, Shin | - |
dc.contributor.author | Kwon, Taesoo | - |
dc.contributor.author | Oba, Shigeyuki | - |
dc.date.accessioned | 2022-07-07T11:18:45Z | - |
dc.date.available | 2022-07-07T11:18:45Z | - |
dc.date.created | 2021-05-11 | - |
dc.date.issued | 2020-11 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/144453 | - |
dc.description.abstract | Motion synthesis benefits from the use of motion capture data and a dynamic model because the motion data can provide a reference to naturalness, and the dynamic model can support environmental constraints such as footskate prevention or perturbation response. However, a combination of a dynamic model and captured motion usually demands professional insights, experience, and additional efforts such as preprocessing or off-line optimization. To address this issue, we propose a modularized predictive coding-based motion synthesis framework that synthesizes natural motion while maintaining the constraints. Modularized predictive coding provides intuitive online mediation of multiple information sources, which can then be applied to motion synthesis. To validate the proposed framework, we applied different types of motion data and character models to synthesize human walking, kickboxing, and backflipping motions, a dog walking motion, and a hand object-grasping motion. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Modularized Predictive Coding-Based Online Motion Synthesis Combining Environmental Constraints and Motion-Capture Data | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kwon, Taesoo | - |
dc.identifier.doi | 10.1109/ACCESS.2020.3036449 | - |
dc.identifier.scopusid | 2-s2.0-85096335893 | - |
dc.identifier.wosid | 000589786100001 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.8, pp.202274 - 202285 | - |
dc.relation.isPartOf | IEEE ACCESS | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 8 | - |
dc.citation.startPage | 202274 | - |
dc.citation.endPage | 202285 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | ANIMATION | - |
dc.subject.keywordAuthor | Combination of linear models | - |
dc.subject.keywordAuthor | hybrid-based character animation | - |
dc.subject.keywordAuthor | neuroscience-inspired | - |
dc.subject.keywordAuthor | online motion synthesis | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9250538 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.