Tracking body fat percentage is essential for effective weight management, yet gold-standard methods such as DEXA scans remain expensive and inaccessible for most people. This study evaluates the feasibility of artificial intelligence (AI) models as low-cost alternatives using frontal body images and basic anthropometric data. The dataset consists of 535 samples: 253 cases with recorded anthropometric measurements (weight, height, neck, ankle, and wrist) and 282 images obtained via web scraping from Reddit posts with self-reported body fat percentages, including some reported as DEXA-derived by the original posters. Because no public datasets exist for computer-vision-based body fat estimation, this dataset was compiled specifically for this study. Two approaches were developed: (1) ResNet-based image models and (2) regression models using anthropometric measurements. A multimodal fusion framework is also outlined for future expansion once paired datasets become available. The image-based model achieved a Root Mean Square Error (RMSE) of 4.44% and a Coefficient of Determination (R^2) of 0.807. These findings demonstrate that AI-assisted models can offer accessible and low-cost body fat estimates, supporting future consumer applications in health and fitness.

We introduce a novel technique called ShapedNet to enhance body composition assessment. This method employs a deep neural network capable of estimating Body Fat Percentage (BFP), performing individual identification, and enabling localization using a single photograph. The accuracy of ShapedNet is validated through comprehensive comparisons against the gold standard method, Dual-Energy X-ray Absorptiometry (DXA), utilizing 1273 healthy adults spanning various ages, sexes, and BFP levels. The results demonstrate that ShapedNet outperforms in 19.5% state of the art computer vision-based approaches for body fat estimation, achieving a Mean Absolute Percentage Error (MAPE) of 4.91% and Mean Absolute Error (MAE) of 1.42. The study evaluates both gender-based and Gender-neutral approaches, with the latter showcasing superior performance. The method estimates BFP with 95% confidence within an error margin of 4.01% to 5.81%. This research advances multi-task learning and body composition assessment theory through ShapedNet.