基于改进深度学习模型的高精度服装样板自动生成  

Automatic generation of high-precision garment patterns based on improved deep learning model

作　　者：黄小源 侯珏 杨阳[2,3] 刘正 HUANG Xiaoyuan;HOU Jue;YANG Yang;LIU Zheng(College of Textile Science and Engineering(International Institute of Silk),Zhejiang Sci-Tech University,Hangzhou,Zhejiang 310018,China;School of Fashion Design&Engineering,Zhejiang Sci-Tech University,Hangzhou,Zhejiang 310018,China;Key Laboratory of Silk Culture Inheritance and Digital Technology of Product Design,Ministry of Culture and Tourism,Hangzhou,Zhejiang 310018,China;International Institute of Fashion Technology,Zhejiang Sci-Tech University,Hangzhou,Zhejiang 310018,China)

机构地区：[1]浙江理工大学纺织科学与工程学院(国际丝绸学院),浙江杭州310018 [2]浙江理工大学服装学院,浙江杭州310018 [3]丝绸文化传承与产品设计数字化技术文化和旅游部重点实验室,浙江杭州310018 [4]浙江理工大学国际时装技术学院,浙江杭州310018

出　　处：《纺织学报》2025年第2期236-243,共8页Journal of Textile Research

基　　金：浙江省科技重点研发计划项目(2023C03181);嘉兴市重点研发计划项目(2023BZ10009)。

摘　　要：针对三维服装转换成二维样板过程缺乏考虑服装专业知识,导致样板精度差而无法直接应用的问题,提出一种基于深度学习和专家知识相结合的三维服装高精度样板的自动生成方法。首先,通过添加三次和四次贝塞尔曲线以及直角化约束改进服装样板数据集生成器,生成专业高精度样板和三维服装模型数据集;设置边缘损失改进二维样板生成的深度学习混合框架模型,再结合服装结构设计专家知识对生成样板的边缘细节进行优化;最后采用物理模拟和现实扫描三维服装模型进行实例验证。结果表明:改进后的模型在预测样板形状、样板位置、边数准确率等评价指标上均有显著提高,在测试集上样板形状的均方误差降至1.59 cm,精度符合服装相应部位的公允差范围,且对物理模拟和真实扫描的三维服装样板预测具有较好的吻合度,为专业服装样板自动生成提供了有效途径。Objective The generation of garment patterns has long been an important research focus in the field of garment product development and garment CAD.Addressing the issue of poor pattern accuracy due to the lack of consideration of garment-specific knowledge during the conversion of 3-D garments into 2-D patterns,which results in patterns that cannot be directly applied,this paper proposes an automatic method for high-precision 3-D garment pattern generation based on the combination of deep learning and expert knowledge.Method This paper adopted a deep learning-based approach,improving the model by integrating garment pattern requirements and expert knowledge into the NeuralTailor hybrid framework.As the first step,cubic and quartic Bezier curves,as well as right-angle constraints,were added to enhance the garment pattern dataset generator,producing a professional high-precision pattern and 3-D garment model dataset,solving the previous issue of accurately representing complex curves in garment patterns.Then,an edge length loss function was introduced in the training loss of the NeuralTailor framework.Combined with expert knowledge of garment structural design,a fuzzy mathematical model was used to assess garment fit,adjusting the corresponding pattern arcs and optimizing edge details of the generated patterns.This made the improved model capable of automatically generating more precise patterns that better met industrial application requirements.Finally,physical simulations and real-world scanned 3-D garment models were used for case validation.Results The improved model was evaluated through comparative experiments.Quantitative analysis of the evaluation metrics for different models showed that the pattern shape error of this model was reduced by 0.69 cm compared to the pre-improvement model,with the pattern shape error being less than 2 cm,which falls within the acceptable bust tolerance range for garment production.Translation and rotation errors were also reduced,and the accuracy of the number of pattern edges

关 键 词：三维服装 样板生成 专家知识 深度学习 服装数字化建模 

分 类 号：TS941[轻工技术与工程—服装设计与工程]