In real life, people’s life gradually tends to be simple, so the convenience of online shopping makes more and more research begin to explore the convenience optimization of shopping, in which the fitting system is the research product. However, due to the immaturity of the virtual fitting system, there are a lot of problems, such as the expression of clothing color is not clear or deviation. In view of this, this paper proposes a 3D clothing color display model based on deep learning to support human modeling-driven. Firstly, the macro-micro adversarial network (MMAN) based on deep learning is used to analyze the original image, and then, the results are preprocessed. Finally, the 3D model with the original image color is constructed by using UV mapping. The experimental results show that the accuracy of the MMAN algorithm reaches 0.972, the established three-dimensional model is emotional enough, the expression of the clothing color is clear, and the difference between the color difference and the original image is within 0.01, and the subjective evaluation of volunteers is more than 90 points. The above results show that it is effective to use deep learning to build a 3D model with the original picture clothing color, which has great guiding significance for the research of character model modeling and simulation.

中文翻译：

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基于深度学习宏微观对抗网络和人体建模的3D服装色彩应用研究

在现实生活中，人们的生活逐渐趋于简单，网络购物的便利性使得越来越多的研究开始探索购物的便利性优化，其中试衣系统就是研究产品。但由于虚拟试衣系统尚不成熟，存在服装颜色表达不清晰或偏差等诸多问题。鉴于此，本文提出一种基于深度学习的支持人体建模驱动的3D服装色彩显示模型。首先，利用基于深度学习的宏微观对抗网络（MMAN）对原始图像进行分析，然后对结果进行预处理。最后，使用UV映射构建具有原始图像颜色的3D模型。实验结果表明，MMAN算法准确率达到0.972，建立的三维模型足够情感化，服装颜色表达清晰，色差与原图相差在0.01以内，志愿者主观评价90分以上。上述结果表明，利用深度学习构建原图服装颜色的3D模型是有效的，对于人物模型建模与仿真的研究具有重要的指导意义。