Motion Retargetting based on Dilated Convolutions and Skeleton-specific Loss Functions
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, SangBin | - |
dc.contributor.author | Park, Inbum | - |
dc.contributor.author | Kwon, Seongsu | - |
dc.contributor.author | Han, JungHyun | - |
dc.date.accessioned | 2021-08-31T01:34:19Z | - |
dc.date.available | 2021-08-31T01:34:19Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2020-05 | - |
dc.identifier.issn | 0167-7055 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/56156 | - |
dc.description.abstract | Motion retargetting refers to the process of adapting the motion of a source character to a target. This paper presents a motion retargetting model based on temporal dilated convolutions. In an unsupervised manner, the model generates realistic motions for various humanoid characters. The retargetted motions not only preserve the high-frequency detail of the input motions but also produce natural and stable trajectories despite the skeleton size differences between the source and target. Extensive experiments are made using a 3D character motion dataset and a motion capture dataset. Both qualitative and quantitative comparisons against prior methods demonstrate the effectiveness and robustness of our method. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | WILEY | - |
dc.subject | HUMAN POSE ESTIMATION | - |
dc.subject | NEURAL-NETWORKS | - |
dc.title | Motion Retargetting based on Dilated Convolutions and Skeleton-specific Loss Functions | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Han, JungHyun | - |
dc.identifier.doi | 10.1111/cgf.13947 | - |
dc.identifier.scopusid | 2-s2.0-85087780809 | - |
dc.identifier.wosid | 000548709600041 | - |
dc.identifier.bibliographicCitation | COMPUTER GRAPHICS FORUM, v.39, no.2, pp.497 - 507 | - |
dc.relation.isPartOf | COMPUTER GRAPHICS FORUM | - |
dc.citation.title | COMPUTER GRAPHICS FORUM | - |
dc.citation.volume | 39 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 497 | - |
dc.citation.endPage | 507 | - |
dc.type.rims | ART | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.subject.keywordPlus | HUMAN POSE ESTIMATION | - |
dc.subject.keywordPlus | NEURAL-NETWORKS | - |
dc.subject.keywordAuthor | CCS Concepts | - |
dc.subject.keywordAuthor | . Computing methodologies -> | - |
dc.subject.keywordAuthor | Neural networks | - |
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