Design of underclothing 3D human body measurement and reconstruction system for clothed individuals

Obtaining accurate naked 3D body models and measurements in a clothed state is desirable for the apparel industry. In this paper, deep learning, body scanning, and millimeter-wave measurement techniques are combined to design a system that can accurately reconstruct a personalized naked human model even when heavy or loose clothing is worn. The system first acquires a frontal point cloud using depth cameras, then predicts the underclothing body shape through deep learning and registration with a parametric model. Millimeter-wave sensors are then utilized to precisely measure anthropometric parameters of key body parts, providing additional constraints for reconstruction. Finally, the parameters of the parametric model are optimized to match the measurements, obtaining a more accurate 3D reconstruction. Qualitative and quantitative experiments show that the mean and median error values of the reconstructed body key parts (chest circumference, waist circumference, hip circumference) are less than 2 cm, which gives the system an obvious advantage in reconstruction accuracy and robustness compared to deep learning methods alone. In addition, the reconstructed human body model is parametric, and the system can be applied not only in the apparel industry, but also has the potential in film and animation and other fields.