OperaGAN:用于歌剧化妆和复杂头饰的同步传输网络。

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yue Ma, Chunjie Xu, Wei Song, Hanyu Liang
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引用次数: 0

摘要

标准的化妆转移技术主要侧重于面部化妆。风格范例中头饰的纹理细节往往被忽视。在处理复杂的人像风格转移时,往往无法保证同时正确地转移头饰和面部妆容。本文构建了京剧化妆数据集,并提出了一种名为 OperaGAN 的京剧脸谱化妆转移网络。该网络由两个关键部分组成:化妆和头饰风格编码器模块(MHSEnc)以及身份编码和化妆融合模块(ICMF)。MHSEnc 专门用于从全局和局部角度提取风格特征。ICMF 提取源图像的面部特征,并将其与风格特征相结合,生成最终的传输结果。此外,我们还利用多个重叠的局部判别器来转移戏曲化妆中的高频细节。实验证明,我们的方法在同时传输戏曲妆容和头饰方面达到了最先进的效果。此外,该方法还能传输内容缺失的头饰和强度可控的妆容。代码和数据集将发布在 https://github.com/Ivychun/OperaGAN 网站上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
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.
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