生成对抗网络在虚拟试衣中的应用研究进展

Research progress on the application of generative adversarial network in virtual fitting

生成对抗网络(GAN)的出色性能,使得深度学习在虚拟试衣中的应用得到新的发展,可以应对虚拟试衣中诸多问题和需求。GAN能够产生高度真实的输出,与原始训练分布非常接近,成为当下实现虚拟试衣不容忽视的工具。文章围绕这一研究前沿与热点问题,对GAN及其在虚拟试衣中的发展进行了简单的回顾;其次从GAN生成虚拟试衣结果的特征类别出发,先后介绍了在2D图像、3D模型及视频的虚拟试衣上的应用,总结分析了它们的运作机制、优点、局限性及适用场景;最后,讨论了GAN在虚拟试衣领域未来的研究方向。研究认为,未来可在增加试穿服装件数、提高试穿图像分辨率和准确性、提高视频试穿速度3个方向开展研究。

The excellent performance of Generative Adversarial Network(GAN)has facilitated the new development and application of deep learning in virtual fitting,which could deal with many problems and needs in virtual fitting.GAN is able to produce highly real output and is very close to the original training distribution,thus becoming a tool that can’t be ignored in the current virtual fitting.Firstly,focusing on this research frontier and hot issue,this paper briefly reviewed GAN and its development in virtual fitting;then,starting from the feature categories of virtual fitting results generated by GAN,the paper introduced its application in virtual fitting based on 2D image,3D model and video,summarized and analyzed the operation mechanism,advantages,limitations and applicable scenarios;finally,it prospected the future development direction of GAN in virtual fitting.It is believed that future research can be carried out in three directions:increasing the number of fitting clothing,raising the resolution and accuracy of fitting image,and improving the speed of video fitting speed.