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DC poleHodnotaJazyk
dc.contributor.authorPeng, Jihua
dc.contributor.authorZhou, Yanghong
dc.contributor.authorMok, P.Y.
dc.contributor.editorSkala, Václav
dc.date.accessioned2022-09-01T09:05:54Z
dc.date.available2022-09-01T09:05:54Z
dc.date.issued2022
dc.identifier.citationWSCG 2022: full papers proceedings: 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 103-108.en
dc.identifier.isbn978-80-86943-33-6
dc.identifier.issn2464-4617
dc.identifier.urihttp://hdl.handle.net/11025/49583
dc.format6 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subject3D odhad lidské pozicecs
dc.subjectseskupení funkce fúzecs
dc.subjectanatomické vztahycs
dc.titleBalanced Feature Fusion for Grouped 3D Pose Estimationen
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translated3D human pose estimation by grouping human body joints according to anatomical relationship is currently a popular and effective method. For grouped pose estimation, fusing features of different groups together effectively is the key step to ensure the integrity of whole body pose prediction. However, the existing methods for feature fusion between groups require a large number of network parameters, and thus are often computational expensive. In this paper, we propose a simple yet efficient feature fusion method that can improve the accuracy of pose esti- mation while require fewer parameters and less calculations. Experiments have shown that our proposed network outperforms previous state-of-the-art results on Human3.6M dataset.en
dc.subject.translated3D human pose estimationen
dc.subject.translatedgrouping feature fusionen
dc.subject.translatedanatomical relationshipsen
dc.identifier.doihttps://www.doi.org/10.24132/CSRN.3201.13
dc.type.statusPeer-revieweden
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  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
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