Abstract
This paper proposes a design method for personalized 3D modeling and a rapid style recommendation of polo shirts based on interactive genetic algorithms. Through the research on the parametric multi-style design method of polo shirts, this paper proposes 3D design methods such as the conversion of 3D models, component splicing, and stripes matching mapping rules. In addition, we establish a coded component library of style parts and striped designs to realize the component modeling of tailor-made styles for polo shirts. Besides, the Interactive Genetic Algorithm (IGA) is introduced in the article. Through the algorithm coding, initial population generation, selection, using the scoring mechanism to obtain the user’s suitability evaluation of the program, crossover, and mutation, etc., gradually generating a user-satisfied polo shirt style model design plan, a personalized rapid style recommendation of the user-oriented polo shirt is established. This system can present tailored polo shirt style for customers, and WYSIWYG, to find the most favorite clothing styles for customers and lower the threshold of clothing design expertise.
Highlights
In recent years, personalized clothing customization has become a new way of producing and selling garments
Based on building a modular and diverse clothing parts library, clothing customization combined with free design can quickly modify and generate virtual design solutions suitable for consumers, and realize the integration of anthropometry, clothing pattern design, modeling, and production
WANG Qian considered the basic model of module configuration from customer demand function, clothing design factors, environmental demand factors, and summarized the main design methods involved in the module construction process.[3]
Summary
In recent years, personalized clothing customization has become a new way of producing and selling garments. This article proposes to generate an initial group by combining the polo shirt components to form a style design and a striped appearance design, obtain individual fitness through interactive evaluation, hybridize and mutate design solutions that do not meet customer needs, and evolve to produce better offspring.
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