Inferring Body Measurements from 2D Images: A Comprehensive Review
The prediction of anthropometric measurements from 2D body images, particularly for children, remains an under-explored area despite its potential applications in healthcare, fashion, and fitness. While pose estimation and body shape classification have garnered extensive attention, estimating body...
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| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/11/6/205 |
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| author | Hezha Mohammedkhan Hein Fleuren Çíçek Güven Eric Postma |
| author_facet | Hezha Mohammedkhan Hein Fleuren Çíçek Güven Eric Postma |
| author_sort | Hezha Mohammedkhan |
| collection | DOAJ |
| description | The prediction of anthropometric measurements from 2D body images, particularly for children, remains an under-explored area despite its potential applications in healthcare, fashion, and fitness. While pose estimation and body shape classification have garnered extensive attention, estimating body measurements and body mass index (BMI) from images presents unique challenges and opportunities. This paper provides a comprehensive review of the current methodologies, focusing on deep-learning approaches, both standalone and in combination with traditional machine-learning techniques, for inferring body measurements from facial and full-body images. We discuss the strengths and limitations of commonly used datasets, proposing the need for more inclusive and diverse collections to improve model performance. Our findings indicate that deep-learning models, especially when combined with traditional machine-learning techniques, offer the most accurate predictions. We further highlight the promise of vision transformers in advancing the field while stressing the importance of addressing model explainability. Finally, we evaluate the current state of the field, comparing recent results and focusing on the deviations from ground truth, ultimately providing recommendations for future research directions. |
| format | Article |
| id | doaj-art-21b9a4db4c2146e4aabce6e71f443bc9 |
| institution | Kabale University |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-21b9a4db4c2146e4aabce6e71f443bc92025-08-20T03:27:33ZengMDPI AGJournal of Imaging2313-433X2025-06-0111620510.3390/jimaging11060205Inferring Body Measurements from 2D Images: A Comprehensive ReviewHezha Mohammedkhan0Hein Fleuren1Çíçek Güven2Eric Postma3Department of Cognitive Science and Artificial Intelligence, School of Humanities and Digital Sciences, Tilburg University, 5037 AB Tilburg, The NetherlandsZero Hungerlab, Department of Econometrics and Operations Research, School of Economics and Management, Tilburg University, 5037 AB Tilburg, The NetherlandsDepartment of Cognitive Science and Artificial Intelligence, School of Humanities and Digital Sciences, Tilburg University, 5037 AB Tilburg, The NetherlandsDepartment of Cognitive Science and Artificial Intelligence, School of Humanities and Digital Sciences, Tilburg University, 5037 AB Tilburg, The NetherlandsThe prediction of anthropometric measurements from 2D body images, particularly for children, remains an under-explored area despite its potential applications in healthcare, fashion, and fitness. While pose estimation and body shape classification have garnered extensive attention, estimating body measurements and body mass index (BMI) from images presents unique challenges and opportunities. This paper provides a comprehensive review of the current methodologies, focusing on deep-learning approaches, both standalone and in combination with traditional machine-learning techniques, for inferring body measurements from facial and full-body images. We discuss the strengths and limitations of commonly used datasets, proposing the need for more inclusive and diverse collections to improve model performance. Our findings indicate that deep-learning models, especially when combined with traditional machine-learning techniques, offer the most accurate predictions. We further highlight the promise of vision transformers in advancing the field while stressing the importance of addressing model explainability. Finally, we evaluate the current state of the field, comparing recent results and focusing on the deviations from ground truth, ultimately providing recommendations for future research directions.https://www.mdpi.com/2313-433X/11/6/205deep learningconvolutional neural networkautomated anthropometryartificial intelligence for nutrition |
| spellingShingle | Hezha Mohammedkhan Hein Fleuren Çíçek Güven Eric Postma Inferring Body Measurements from 2D Images: A Comprehensive Review Journal of Imaging deep learning convolutional neural network automated anthropometry artificial intelligence for nutrition |
| title | Inferring Body Measurements from 2D Images: A Comprehensive Review |
| title_full | Inferring Body Measurements from 2D Images: A Comprehensive Review |
| title_fullStr | Inferring Body Measurements from 2D Images: A Comprehensive Review |
| title_full_unstemmed | Inferring Body Measurements from 2D Images: A Comprehensive Review |
| title_short | Inferring Body Measurements from 2D Images: A Comprehensive Review |
| title_sort | inferring body measurements from 2d images a comprehensive review |
| topic | deep learning convolutional neural network automated anthropometry artificial intelligence for nutrition |
| url | https://www.mdpi.com/2313-433X/11/6/205 |
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