A Survey of Recent Advances on Two-Step 3D Human Pose Estimation
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-031-21689-3_20 http://hdl.handle.net/11449/249522 |
Resumo: | Human pose estimation in images is an important and challenging problem in Computer Vision. Currently, methods that employ deep learning techniques excel in the task of 2D human pose estimation. 2D human poses can be used in a diverse and broad set of applications, of great relevance to society. However, the use of 3D poses can lead to even more accurate and robust results. Since joint coordinates for 3D poses are difficult to estimate, fully convolutional methods tend to perform poorly. One possible solution is to estimate 3D poses based on 2D poses, which offer improved performance by delegating the exploration of image features to more mature 2D pose estimation techniques. The goal of this paper is to present a survey of recent advances on two-step techniques for 3D human pose estimation based on 2D human poses. |
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A Survey of Recent Advances on Two-Step 3D Human Pose Estimation2D human poses3D human pose estimationTwo-step 3D human pose estimationHuman pose estimation in images is an important and challenging problem in Computer Vision. Currently, methods that employ deep learning techniques excel in the task of 2D human pose estimation. 2D human poses can be used in a diverse and broad set of applications, of great relevance to society. However, the use of 3D poses can lead to even more accurate and robust results. Since joint coordinates for 3D poses are difficult to estimate, fully convolutional methods tend to perform poorly. One possible solution is to estimate 3D poses based on 2D poses, which offer improved performance by delegating the exploration of image features to more mature 2D pose estimation techniques. The goal of this paper is to present a survey of recent advances on two-step techniques for 3D human pose estimation based on 2D human poses.Faculty of Sciences UNESP - São Paulo State University, SPFaculty of Sciences UNESP - São Paulo State University, SPUniversidade Estadual Paulista (UNESP)Manesco, João Renato Ribeiro [UNESP]Marana, Aparecido Nilceu [UNESP]2023-07-29T16:01:59Z2023-07-29T16:01:59Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject266-281http://dx.doi.org/10.1007/978-3-031-21689-3_20Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 13654 LNAI, p. 266-281.1611-33490302-9743http://hdl.handle.net/11449/24952210.1007/978-3-031-21689-3_202-s2.0-85145253261Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)info:eu-repo/semantics/openAccess2024-04-23T16:11:33Zoai:repositorio.unesp.br:11449/249522Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:11:33Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A Survey of Recent Advances on Two-Step 3D Human Pose Estimation |
title |
A Survey of Recent Advances on Two-Step 3D Human Pose Estimation |
spellingShingle |
A Survey of Recent Advances on Two-Step 3D Human Pose Estimation Manesco, João Renato Ribeiro [UNESP] 2D human poses 3D human pose estimation Two-step 3D human pose estimation |
title_short |
A Survey of Recent Advances on Two-Step 3D Human Pose Estimation |
title_full |
A Survey of Recent Advances on Two-Step 3D Human Pose Estimation |
title_fullStr |
A Survey of Recent Advances on Two-Step 3D Human Pose Estimation |
title_full_unstemmed |
A Survey of Recent Advances on Two-Step 3D Human Pose Estimation |
title_sort |
A Survey of Recent Advances on Two-Step 3D Human Pose Estimation |
author |
Manesco, João Renato Ribeiro [UNESP] |
author_facet |
Manesco, João Renato Ribeiro [UNESP] Marana, Aparecido Nilceu [UNESP] |
author_role |
author |
author2 |
Marana, Aparecido Nilceu [UNESP] |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Manesco, João Renato Ribeiro [UNESP] Marana, Aparecido Nilceu [UNESP] |
dc.subject.por.fl_str_mv |
2D human poses 3D human pose estimation Two-step 3D human pose estimation |
topic |
2D human poses 3D human pose estimation Two-step 3D human pose estimation |
description |
Human pose estimation in images is an important and challenging problem in Computer Vision. Currently, methods that employ deep learning techniques excel in the task of 2D human pose estimation. 2D human poses can be used in a diverse and broad set of applications, of great relevance to society. However, the use of 3D poses can lead to even more accurate and robust results. Since joint coordinates for 3D poses are difficult to estimate, fully convolutional methods tend to perform poorly. One possible solution is to estimate 3D poses based on 2D poses, which offer improved performance by delegating the exploration of image features to more mature 2D pose estimation techniques. The goal of this paper is to present a survey of recent advances on two-step techniques for 3D human pose estimation based on 2D human poses. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 2023-07-29T16:01:59Z 2023-07-29T16:01:59Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/978-3-031-21689-3_20 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 13654 LNAI, p. 266-281. 1611-3349 0302-9743 http://hdl.handle.net/11449/249522 10.1007/978-3-031-21689-3_20 2-s2.0-85145253261 |
url |
http://dx.doi.org/10.1007/978-3-031-21689-3_20 http://hdl.handle.net/11449/249522 |
identifier_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 13654 LNAI, p. 266-281. 1611-3349 0302-9743 10.1007/978-3-031-21689-3_20 2-s2.0-85145253261 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
266-281 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
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1799965460554317824 |