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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45CUNP8
Repositorysid.inpe.br/sibgrapi/2021/09.06.21.37
Last Update2021:09.06.21.37.12 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/09.06.21.37.12
Metadata Last Update2022:06.14.00.00.32 (UTC) administrator
DOI10.1109/SIBGRAPI54419.2021.00042
Citation KeySchirmer:2021:SeGrAt
TitleSGAT: Semantic Graph Attention for 3D human pose estimation
FormatOn-line
Year2021
Access Date2024, May 06
Number of Files1
Size26659 KiB
2. Context
AuthorSchirmer, Luiz
AffiliationPUC-Rio
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 Addressschirmer.luizj@gmail.com
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado, RS, Brazil (virtual)
Date18-22 Oct. 2021
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2021-09-06 21:37:12 :: schirmer.luizj@gmail.com -> administrator ::
2022-03-02 00:54:16 :: administrator -> menottid@gmail.com :: 2021
2022-03-02 13:36:49 :: menottid@gmail.com -> administrator :: 2021
2022-06-14 00:00:32 :: administrator -> :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsGraph Neural Networks
Pose estimation
Animation
Motion Capture
AbstractWe propose a novel gating mechanism applied to Semantic Graph Convolutions for 3D applications, named Semantic Graph Attention. Semantic Graph Convolutions learn to capture semantic information such as local and global node relationships, not explicitly represented in graphs. We improve their performance by proposing an attention block to explore channel-wise inter-dependencies. The proposed method performs the unprojection of the points 2d (image) in their 3D version (3d scene). We use it to estimate 3d human pose from 2d images. Both 2D and 3D human poses can be represented as structured graphs, and we explore their particularities in this context. The attention layer improves skeleton estimation accuracy using 58\% fewer parameters than state-of-the-art.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2021 > SGAT: Semantic Graph...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > SGAT: Semantic Graph...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/45CUNP8
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45CUNP8
Languageen
Target FileSibgrapi21_final.pdf
User Groupschirmer.luizj@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/45PQ3RS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2021/11.12.11.46 4
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination 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 volume


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