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Ahn, Hyemin
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dc.citation.endPage 3508 -
dc.citation.number 2 -
dc.citation.startPage 3501 -
dc.citation.volume 5 - Ahn, Hyemin - Kim, Jaehun - Kim, Kihyun - Oh, Songhwai - 2023-12-21T17:40:08Z - 2023-12-21T17:40:08Z - 2022-06-08 - 2020-04 -
dc.description.abstract This letter proposes a framework which is able to generate a sequence of three-dimensional human dance poses for a given music. The proposed framework consists of three components: a music feature encoder, a pose generator, and a music genre classifier. We focus on integrating these components for generating a realistic 3D human dancing move from music, which can be applied to artificial agents and humanoid robots. The trained dance pose generator, which is a generative autoregressive model, is able to synthesize a dance sequence longer than 1,000 pose frames. Experimental results of generated dance sequences from various songs show how the proposed method generates human-like dancing move to a given music. In addition, a generated 3D dance sequence is applied to a humanoid robot, showing that the proposed framework can make a robot to dance just by listening to music. -
dc.identifier.bibliographicCitation IEEE ROBOTICS AND AUTOMATION LETTERS, v.5, no.2, pp.3501 - 3508 -
dc.identifier.doi 10.1109/LRA.2020.2977333 -
dc.identifier.issn 2377-3766 -
dc.identifier.scopusid 2-s2.0-85082387193 -
dc.identifier.uri -
dc.identifier.wosid 000522360200001 -
dc.language 영어 -
dc.title Generative Autoregressive Networks for 3D Dancing Move Synthesis From Music -
dc.type Article -
dc.description.isOpenAccess FALSE -
dc.relation.journalWebOfScienceCategory Robotics -
dc.relation.journalResearchArea Robotics -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor Three-dimensional displays -
dc.subject.keywordAuthor Generators -
dc.subject.keywordAuthor Task analysis -
dc.subject.keywordAuthor Multiple signal classification -
dc.subject.keywordAuthor Skeleton -
dc.subject.keywordAuthor Training -
dc.subject.keywordAuthor Music -
dc.subject.keywordAuthor Gesture -
dc.subject.keywordAuthor posture and facial expressions -
dc.subject.keywordAuthor novel deep learning methods -
dc.subject.keywordAuthor entertainment robotics -


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