General Graduate School of Keimyung University South Korea, Daegu 42601, Republic of Korea.
School of Design, Sichuan Fine Arts Institute, Chongqing 401331, China.
Comput Intell Neurosci. 2021 Sep 7;2021:9918175. doi: 10.1155/2021/9918175. eCollection 2021.
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.
在现实生活中，人们的生活逐渐趋于简单，因此在线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，其中虚拟试衣系统就是研究产物。然而，由于虚拟试衣系统不够成熟，存在很多问题，例如服装颜色的表达不够清晰或存在偏差。针对这一问题，本文提出了一种基于深度学习的 3D 服装颜色显示模型，支持人体建模驱动。首先，使用基于深度学习的宏微对抗网络（MMAN）分析原始图像，然后对结果进行预处理。最后，通过 UV 映射构建具有原始图像颜色的 3D 模型。实验结果表明，MMAN 算法的准确率达到 0.972，建立的三维模型具有足够的情感，服装颜色的表达清晰，色差与原始图像的差异在 0.01 以内，志愿者的主观评价超过 90 分。上述结果表明，使用深度学习构建具有原始图片服装颜色的 3D 模型是有效的，这对人物模型建模和模拟的研究具有重要的指导意义。
