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http://bura.brunel.ac.uk/handle/2438/28655
Title: | From prediction to measurement, an efficient method for digital human model obtainment |
Authors: | Wang, M Yang, Q |
Keywords: | digital human;deep learning;computer vision;data analysis |
Issue Date: | 23-Jan-2024 |
Publisher: | EDP Sciences |
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. |
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. |
URI: | http://bura.brunel.ac.uk/handle/2438/28655 |
DOI: | http://dx.doi.org/10.1051/ijmqe/2023015 |
ISSN: | 2107-6839 |
Other Identifiers: | ORCiD: Qingping Yang https://orcid.org/0000-0002-2557-8752 1 |
Appears in Collections: | Dept of Mechanical and Aerospace Engineering Research Papers |
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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 |
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