Weight and Height Estimation from a Single Human Image Captured in the Wild
This paper explores deep neural networks for estimating BMI, weight, and height from a single daily-life image. The authors introduce a new dataset of 6,105 diverse in-the-wild images with ground truth labels, and evaluate multi-modal inputs including RGB, depth maps, pose-affinity maps, and edge maps. Experimental results show that full-body images outperform half-body or face-only images.
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[Submitted on 28 Jul 2026]
Title:Weight and Height Estimation from a Single Human Image Captured in the Wild
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Abstract:A person's physical characteristics such as weight and height are important indicators of his physical and mental health, daily life routines and finances. Body Mass Index (BMI) is a well known measure that encodes the characteristics of both the weight and the height. BMI has been used as a self-monitoring tool, and it has long-term implications on one's life. For example, it may help predicting the risk of various diseases and estimating longevity. Automatic BMI estimation using a single person image in the wild is a challenging task due to wide variations in human pose, camera geometry, personal appearance and distracting backgrounds. In this paper, we explore the performance of deep neural networks using single and multi-task learning by employing different modalities including RGB, depth-maps, pose-affinity maps, and edge-maps to predict BMI, weight, and height from daily life images available on social networking websites. Currently, no full body image dataset for BMI estimation is publicly available, therefore we propose a new dataset consisting of 6105 images with ground truth labels of height, weight and BMI. Our proposed dataset is collected in the wild containing images from various ethnicity and distributed over varying age groups and gender. It consists of frontal, back, full and half body, side poses, mirror selfies with varying backgrounds and scale variations and may contain artifacts hiding partial or full face. Extensive experimentation is performed using full body, half body and face images only using different CNN backbones including VGG, Densenet and ResNet. Our experimental results demonstrate that full body images have produced better results than the other half body and facial images in the wild.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2607.26104 [cs.CV]
(or arXiv:2607.26104v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.26104
arXiv-issued DOI via DataCite
Submission history
From: Arif Mahmood [view email] [v1] Tue, 28 Jul 2026 08:39:11 UTC (17,762 KB)
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