A Survey of Recent Advances on Two-Step 3D Human Pose Estimation

Detalhes bibliográficos
Autor(a) principal: Manesco, João Renato Ribeiro [UNESP]
Data de Publicação: 2022
Outros Autores: Marana, Aparecido Nilceu [UNESP]
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.
id UNSP_54ba0c029b50e7bfe14de42ddf96a94a
oai_identifier_str oai:repositorio.unesp.br:11449/249522
network_acronym_str UNSP
network_name_str Repositório Institucional da UNESP
repository_id_str 2946
spelling 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
_version_ 1799965460554317824