An overview of state-of-the-art methods for 3D human pose estimation from monocular video
Human pose estimation (HPE) determines the configuration of human body components from a given input, particularly images and videos. The goal of this thesis is to present a comprehensive review of most commonly approaches used to address this subject, which has been widely explored in computer visi...
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| Tipo de documento: | dissertação |
| Data de publicação: | 2023 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositório: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglês |
| OAI Identifier: | oai:upcommons.upc.edu:2117/401727 |
| Acesso em linha: | https://hdl.handle.net/2117/401727 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Computer vision HPE 3D HPE Human Pose Estimation Visió per ordinador Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors |
| Resumo: | Human pose estimation (HPE) determines the configuration of human body components from a given input, particularly images and videos. The goal of this thesis is to present a comprehensive review of most commonly approaches used to address this subject, which has been widely explored in computer vision literature. This essay focuses on the state-of-the-art of methods for computing 3D Human Pose Estimation from monocular video, starting with a comprehensive review of the most recent deep learning-based solutions used for 2D and 3D pose estimation, delving into the main 3D HPE techniques and finally carrying out a qualitative comparison of the latest and most widely used models on benchmark datasets. |
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