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DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 3508 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 3501 | - |
dc.citation.title | IEEE ROBOTICS AND AUTOMATION LETTERS | - |
dc.citation.volume | 5 | - |
dc.contributor.author | Ahn, Hyemin | - |
dc.contributor.author | Kim, Jaehun | - |
dc.contributor.author | Kim, Kihyun | - |
dc.contributor.author | Oh, Songhwai | - |
dc.date.accessioned | 2023-12-21T17:40:08Z | - |
dc.date.available | 2023-12-21T17:40:08Z | - |
dc.date.created | 2022-06-08 | - |
dc.date.issued | 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 | https://scholarworks.unist.ac.kr/handle/201301/58678 | - |
dc.identifier.wosid | 000522360200001 | - |
dc.language | 영어 | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
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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