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DC Field | Value | Language |
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dc.contributor.author | Wang, M | - |
dc.contributor.author | Yang, Q | - |
dc.date.accessioned | 2024-03-29T15:37:40Z | - |
dc.date.available | 2024-01-01 | - |
dc.date.available | 2024-03-29T15:37:40Z | - |
dc.date.issued | 2024-01-23 | - |
dc.identifier | ORCiD: Qingping Yang https://orcid.org/0000-0002-2557-8752 | - |
dc.identifier | 1 | - |
dc.identifier.citation | Wang, M. and Yang, Q. (2024) 'From prediction to measurement, an efficient method for digital human model obtainment', International Journal of Metrology and Quality Engineering, 15, 1, pp. 1 - 7. doi: 10.1051/ijmqe/2023015. | en_US |
dc.identifier.issn | 2107-6839 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/28655 | - |
dc.description.abstract | Digital human has been increasingly used in industry, for example in Metaverse which has been a popular topic in recent years. The existing method of obtaining digital human models are either expensive or lack of accuracy. In this paper, we discuss a novel method to reconstruct a 3D human model from 2D images captured by a monocular camera. The input of our method only requires a set of rotated human body images that can accept slight movement. First, we apply a deep learning method to predict an initial 3D human body model from multi-view human body images. Then the total detailed digital human model will be computed and optimized. The typical method requires the human body and cameras fixed to obtain a visual hull from a significant number of camera images. This could be extremely expensive and inconvenient when such an application is developed for online users. Compared to the structural lighting measurement system, our predict-optimized framework only requires several input images captured by personal equipment to provide enough accuracy and online use resolution results. | en_US |
dc.format.extent | 1 - 7 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en | en_US |
dc.publisher | EDP Sciences | en_US |
dc.rights | Copyright © M. Wang and Q. Yang, Published by EDP Sciences, 2024. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | - |
dc.subject | digital human | en_US |
dc.subject | deep learning | en_US |
dc.subject | computer vision | en_US |
dc.subject | data analysis | en_US |
dc.title | From prediction to measurement, an efficient method for digital human model obtainment | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1051/ijmqe/2023015 | - |
dc.relation.isPartOf | International Journal of Metrology and Quality Engineering | - |
pubs.publication-status | Published | - |
pubs.volume | 15 | - |
dc.identifier.eissn | 2107-6847 | - |
dc.rights.license | https://creativecommons.org/licenses/by/4.0/legalcode.en | - |
dc.rights.holder | M. Wang and Q. Yang | - |
Appears in Collections: | Dept of Mechanical and Aerospace Engineering Research Papers |
Files in This Item:
File | Description | Size | Format | |
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FullText.pdf | Copyright © M. Wang and Q. Yang, Published by EDP Sciences, 2024. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | 511.86 kB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License