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This study looks at two aspects of ancient Chinese genre painting to interpret the more realistic ancient shopping scenes in the painting of the peddler in spring scenery in the Ming Dynasty, namely, the Ming spring scene and the characters’ costume image features. Through the analysis of the detailed characteristics of the style, structure, pattern, and color of the figure costume in the painting of the peddler, a total of five sets of figure costume for an adult male and four children in the painting of the peddler in the spring scenery of the Ming Dynasty in China were digitally restored using the 3D virtual fitting technology, which clearly showed the characteristics of the figure costume of the peddler and children in the Ming Dynasty (1368–1644). The figure’s costume style in the painting of the salesman restored in the article is Beizi, closed crotch trousers, open crotch trousers, and abdominal circumference (a type of apron from the Song Dynasty. It is made to resemble a short apron and is wrapped around the abdomen). The study of the painting of peddler in the Ming dynasty in this work provides a reference for future study on ancient Chinese genre paintings of peddler and the restoration of the costumes of the figures in the painting.

This research presented a novel method using 3D simulation methods to design customized garments for physically disabled people with scoliosis (PDPS). The proposed method is based on the virtual human model created from 3D scanning, permitting to simulate the consumer’s morphological shape with atypical physical deformations. Next, customized 2D and 3D virtual garment prototyping tools will be used to create products through interactions. The proposed 3D garment design method is based on the concept of knowledge-based design, using the design knowledge and process already applied to normal body shapes successfully. The characters of the PDPS and the relationship between human body and garment are considered in the prototyping process. As a visualized collaborative design process, the communication between designer and consumer is ensured, permitting to adapt the finished product to disabled people afflicted with severe scoliosis.

At present, garment e-commerce is developing rapidly, and the number of online shopping has increased significantly. In this context, the intelligent evaluation technology of garment fit is particularly important. Fit is the most basic requirement in the process of human dressing. It is of great significance to study the relationship between garment fit and human movement, garment structure, and fabric properties. In this article, we propose a garment fit evaluation model based on back propagation artificial neural network. This method realizes the evaluation of garment fit without any tryout. The inputs of the model are the anthropometric data, garment pattern and fabric properties, while the output is the prediction result of garment fit (fit or unfit). In order to build and train the model, the input and output data were obtained by experiment. And a total of 284 experimental samples were obtained. Through the real try-on test, the results revealed that this approach can effectively evaluate the fit of garment. It introduces new ideas and methods for the intelligent evaluation of garment fit, and has a certain reference value for the research of intelligent evaluation technology of garment fit. [ABSTRACT FROM AUTHOR]