  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.
