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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45DAPHE
Repositorysid.inpe.br/sibgrapi/2021/09.08.22.59
Last Update2021:09.08.22.59.27 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/09.08.22.59.27
Metadata Last Update2022:09.10.00.16.17 (UTC) administrator
Citation KeyFerreiraMartNasc:2021:SyReHu
TitleSynthesizing realistic human dance motions conditioned by musical data using graph convolutional networks
FormatOn-line
Year2021
Access Date2024, May 06
Number of Files1
Size16203 KiB
2. Context
Author1 Ferreira, João Pedro Moreira
2 Martins, Renato
3 Nascimento, Erickson Rangel
Affiliation1 Universidade Federal de Minas Gerais
2 Université Bourgogne Franche-Comté
3 Universidade Federal de Minas Gerais
EditorPaiva, Afonso
Menotti, David
Baranoski, Gladimir V. G.
Proença, Hugo Pedro
Junior, Antonio Lopes Apolinario
Papa, João Paulo
Pagliosa, Paulo
dos Santos, Thiago Oliveira
e Sá, Asla Medeiros
da Silveira, Thiago Lopes Trugillo
Brazil, Emilio Vital
Ponti, Moacir A.
Fernandes, Leandro A. F.
Avila, Sandra
e-Mail Addressjoaopmoferreira@gmail.com
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado, RS, Brazil (virtual)
Date18-22 Oct. 2021
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeMaster's or Doctoral Work
History (UTC)2021-09-08 22:59:27 :: joaopmoferreira@gmail.com -> administrator ::
2022-09-10 00:16:17 :: administrator -> :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsHuman motion generation
sound and dance processing
multimodal learning
conditional adversarial nets
graph convolutional neural networks
AbstractLearning to move naturally from music, i.e., to dance, is one of the most complex motions humans often perform effortlessly. Synthesizing human motion through learning techniques is becoming an increasingly popular approach to alleviating the requirement of new data capture to produce animations. Most approaches, addressing the problem of automatic dance motion synthesis with classical convolutional and recursive neural models, undergo training and variability issues due to the non-Euclidean geometry of the motion manifold structure. In this thesis, we design a novel method based on graph convolutional networks, that overcome the aforementioned issues, to tackle the problem of automatic dance generation from audio information. Our method uses an adversarial learning scheme conditioned on the input music audios to create natural motions preserving the key movements of different music styles. We also collected, annotated and made publicly available a novel multimodal dataset with paired audio, motion data and videos of people dancing three different music styles, as a common ground to evaluate dance generation approaches. The results suggest that the proposed GCN model outperforms the state-of-the-art dance generation method conditioned on music in different experiments. Moreover, our graph-convolutional approach is simpler, easier to be trained, and capable of generating more realistic motion styles regarding qualitative and different quantitative metrics. It also presents a visual movement perceptual quality comparable to real motion data. The dataset, source code, and qualitative results are available on the project's webpage: https://verlab.github.io/Learning2Dance_CAG_2020/.
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/45DAPHE
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45DAPHE
Languageen
Target Filewtd-sibgrapi-joao.pdf
User Groupjoaopmoferreira@gmail.com
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/45PQ3RS
Citing Item Listsid.inpe.br/sibgrapi/2021/11.12.11.46 8
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume


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