3D Human pose estimation using geometric self-supervision with temporal methods
dc.contributor.author | Bau, Nandi | |
dc.contributor.supervisor | Klein, Richard | |
dc.date.accessioned | 2025-06-09T16:08:43Z | |
dc.date.issued | 2024-09 | |
dc.description | A dissertation submitted in fulfilment of the requirements for the degree of Master of Science, to the Faculty of Science, School of Computer Science & Applied Mathematics, University of the Witwatersrand, Johannesburg, 2024 | |
dc.description.abstract | This dissertation explores the enhancement of 3D human pose estimation (HPE) through self-supervised learning methods that reduce reliance on heavily annotated datasets. Recognising the limitations of data acquired in controlled lab settings, the research investigates the potential of geometric self-supervision combined with temporal information to improve model performance in real-world scenarios. A Temporal Dilated Convolutional Network (TDCN) model, employing Kalman filter post-processing, is proposed and evaluated on both ground-truth and in-the-wild data from the Human3.6M dataset. The results demonstrate a competitive Mean Per Joint Position Error (MPJPE) of 62.09mm on unseen data, indicating a promising direction for self-supervised learning in 3D HPE and suggesting a viable pathway towards reducing the gap with fully supervised methods. This study underscores the value of self-supervised temporal dynamics in advancing pose estimation techniques, potentially making them more accessible and broadly applicable in real-world applications. | |
dc.description.sponsorship | DSI-NRF Centre of Excellence in Mathematical and Statistical Sciences (CoE-MaSS), South Africa | |
dc.description.submitter | MMM2025 | |
dc.faculty | Faculty of Science | |
dc.identifier | 000-0002-2067-418X | |
dc.identifier.citation | Bau, Nandi. (2024). 3D Human pose estimation using geometric self-supervision with temporal methods. [Masters' dissertation, University of the Witwatersrand, Johannesburg]. WIReDSpace. https://hdl.handle.net/10539/45085 | |
dc.identifier.uri | https://hdl.handle.net/10539/45085 | |
dc.language.iso | en | |
dc.publisher | University of the Witwatersrand, Johannesburg | |
dc.rights | ©2024 University of the Witwatersrand, Johannesburg. All rights reserved. The copyright in this work vests in the University of the Witwatersrand, Johannesburg. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of University of the Witwatersrand, Johannesburg. | |
dc.rights.holder | University of the Witwatersrand, Johannesburg | |
dc.school | School of Computer Science and Applied Mathematics | |
dc.subject | Machine learning | |
dc.subject | Applied machine learning | |
dc.subject | Computer vision | |
dc.subject | Human pose estimation | |
dc.subject | 3D human pose estimation | |
dc.subject | Geometric self-supervision | |
dc.subject | Temporal methods | |
dc.subject | Temporal dilated convolutional neural networks | |
dc.subject | Deep learning | |
dc.subject | UCTD | |
dc.subject.primarysdg | SDG-9: Industry, innovation and infrastructure | |
dc.subject.secondarysdg | SDG-4: Quality education | |
dc.title | 3D Human pose estimation using geometric self-supervision with temporal methods | |
dc.type | Dissertation |