Modification and refinement of three‐dimensional reconstruction to estimate body volume from a simulated single‐camera image

Abstract Objective Body volumes (BV) are used for calculating body composition to perform obesity assessments. Conventional BV estimation techniques, such as underwater weighing, can be difficult to apply. Advanced machine learning techniques enable multiple obesity‐related body measurements to be obtained using a single‐camera image; however, the accuracy of BV calculated using these techniques is unknown. This study aims to adapt and evaluate a machine learning technique, synthetic training for real accurate pose and shape (STRAPS), to estimate BV. Methods The machine learning technique, STRAPS, was applied to generate three‐dimensional (3D) models from simulated two‐dimensional (2D) images; these 3D models were then scaled with body stature and BV were estimated using regression models corrected for body mass. A commercial 3D scan dataset with a wide range of participants (n = 4318) was used to compare reference and estimated BV data. Results The developed methods estimated BV with small relative standard errors of estimation (