{"title":"OperaGAN:用于歌剧化妆和复杂头饰的同步传输网络。","authors":"Yue Ma, Chunjie Xu, Wei Song, Hanyu Liang","doi":"10.1016/j.neunet.2024.107015","DOIUrl":null,"url":null,"abstract":"<p><p>Standard makeup transfer techniques mainly focus on facial makeup. The texture details of headwear in style examples tend to be ignored. When dealing with complex portrait style transfer, simultaneous correct headwear and facial makeup transfer often cannot be guaranteed. In this paper, we construct the Peking Opera makeup dataset and propose a makeup transfer network for Opera faces called OperaGAN. This network consists of two key components: the Makeup and Headwear Style Encoder module (MHSEnc) and the Identity Coding and Makeup Fusion module (ICMF). MHSEnc is specifically designed to extract the style features from global and local perspectives. ICMF extracts the source image's facial features and combines them with the style features to generate the final transfer result. In addition, multiple overlapping local discriminators are utilized to transfer the high-frequency details in opera makeup. Experiments demonstrate that our method achieves state-of-the-art results in simultaneously transferring opera makeup and headwear. And the method can transfer headwear with missing content and controllable intensity makeup. The code and dataset will be available at https://github.com/Ivychun/OperaGAN.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"183 ","pages":"107015"},"PeriodicalIF":6.0000,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"OperaGAN: A simultaneous transfer network for opera makeup and complex headwear.\",\"authors\":\"Yue Ma, Chunjie Xu, Wei Song, Hanyu Liang\",\"doi\":\"10.1016/j.neunet.2024.107015\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Standard makeup transfer techniques mainly focus on facial makeup. The texture details of headwear in style examples tend to be ignored. When dealing with complex portrait style transfer, simultaneous correct headwear and facial makeup transfer often cannot be guaranteed. In this paper, we construct the Peking Opera makeup dataset and propose a makeup transfer network for Opera faces called OperaGAN. This network consists of two key components: the Makeup and Headwear Style Encoder module (MHSEnc) and the Identity Coding and Makeup Fusion module (ICMF). MHSEnc is specifically designed to extract the style features from global and local perspectives. ICMF extracts the source image's facial features and combines them with the style features to generate the final transfer result. In addition, multiple overlapping local discriminators are utilized to transfer the high-frequency details in opera makeup. Experiments demonstrate that our method achieves state-of-the-art results in simultaneously transferring opera makeup and headwear. And the method can transfer headwear with missing content and controllable intensity makeup. The code and dataset will be available at https://github.com/Ivychun/OperaGAN.</p>\",\"PeriodicalId\":49763,\"journal\":{\"name\":\"Neural Networks\",\"volume\":\"183 \",\"pages\":\"107015\"},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2024-12-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neural Networks\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1016/j.neunet.2024.107015\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1016/j.neunet.2024.107015","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
OperaGAN: A simultaneous transfer network for opera makeup and complex headwear.
Standard makeup transfer techniques mainly focus on facial makeup. The texture details of headwear in style examples tend to be ignored. When dealing with complex portrait style transfer, simultaneous correct headwear and facial makeup transfer often cannot be guaranteed. In this paper, we construct the Peking Opera makeup dataset and propose a makeup transfer network for Opera faces called OperaGAN. This network consists of two key components: the Makeup and Headwear Style Encoder module (MHSEnc) and the Identity Coding and Makeup Fusion module (ICMF). MHSEnc is specifically designed to extract the style features from global and local perspectives. ICMF extracts the source image's facial features and combines them with the style features to generate the final transfer result. In addition, multiple overlapping local discriminators are utilized to transfer the high-frequency details in opera makeup. Experiments demonstrate that our method achieves state-of-the-art results in simultaneously transferring opera makeup and headwear. And the method can transfer headwear with missing content and controllable intensity makeup. The code and dataset will be available at https://github.com/Ivychun/OperaGAN.
期刊介绍:
Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.