To improve body-type classification research and personalized clothing, this study adopted a research method of “three-dimensional (3D) scanning + photos” for the body-shape classification of young females’ waist–abdomen–hip. A total of 178 female college students were recruited for manual, photo and 3D body measurement to get the main body information. Based on the data acquired from 3D scanning, the corresponding heights, angles and other parameters of the waist, abdomen and hip were selected and used to analyze the human body in two respects of shape and height. Then the body-shape indexes and the height indexes were respectively analyzed, and 16 shape characteristic indicators and four height characteristic parameters affecting the waist, abdomen and hip were extracted. Three types in shape and two types in height were obtained, and the main classification rules of the waist–abdomen–hip shape were also concluded to identify the body type based on the body measurements extracted from body photos, which realized the automatic body-type identification based on body photos. It was of great practical significance to provide a basis for personalized customization of fast clothing and the subdivision of the human body shape, which could meet the individual customer’s requirements.

The personalized pattern generation method based on 2D body-measuring technology has considerable application potential in clothing e-commerce, remote clothing customization, clothing production, and other aspects. By inputting the front and side body images, this study proposed a new method of generating personalized patterns automatically. The silhouettes could be extracted from the body images to estimate body sizes and design style. The basic rules between the patterns and the body sizes were analysed, and the rules of the general pattern generation were established through a knowledge-based combination of the basic pattern and the style parameters. The sizes extracted from images were compared with the manually measured values, and the errors of these sizes were analysed. Sample pants were made and tried on with the automatic pattern generation system, and the experiments showed that the sample pants have a good fit at some key landmarks. As a result, this system can automatically generate personalized patterns and style designs based on 2D human body images, to improve garment fit and accelerate clothing customization.