3D young female waist-leg modelling based on a hybrid 3d-scan and 2d-image approach

To enhance the pant fit for individuals, this study proposed a method for constructing a 3D waist-leg mannequin based on body images and 3D point-clouds of young women. A total of 288 females aged 18–25 were measured using 3D body scanner and 2D image-shooting method to obtain 3D point-cloud data and body images. The 3D point-cloud data were analyzed to extract 33 sectional curves, including the cross-sectional curves and crotch curve, to identify key points on each curve. For the body images, key parameters related to curve position and shape, such as height, width, and thickness, were automatically extracted for modelling parameters. Curve generation rules were established based on correlation and regression analysis of the key points. The Individualized 3D mannequin was simulated by adjusting the curve centers at each characteristic position to align with the body images, and was validated by comparing the body sizes of the 3D mannequin with the actual measurements. The results indicated that the final simulated mannequin accurately represents the basic characteristics of the waist-leg shape, with significance values above 0.05, which showed the feasibility of the modelling method. Furthermore, 73.9% of the samples had an absolute error of less than 1 cm between the 3D mannequin and the actual measurements. This study can facilitate 3D body modelling from body images, and provide a reference to develop individualized apparel patterns for clothing customization.