Disentangled Representation Learning of Makeup Portraits in the Wild | |
Li, Yi1,2,3,4; Huang, Huaibo1,2,3,4; Cao, Jie1,2,3,4; He, Ran1,2,3,4; Tan, Tieniu1,2,3,4 | |
发表期刊 | International Journal of Computer Vision |
2019-12 | |
卷号 | -期号:-页码:- |
文章类型 | Regular Paper |
摘要 |
Makeup studies have recently caught much attention in computer version. Two of the typical tasks are makeup-invariant face verification and makeup transfer. Although having experienced remarkable progress, both tasks remain challenging, especially encountering data in the wild. In this paper, we propose a disentangled feature learning strategy to fulfil both tasks in a single generative network. Overall, a makeup portrait can be decomposed into three components: makeup, identity and geometry (including expression, pose etc.). We assume that the extracted image representation can be decomposed into a makeup code that captures the makeup style and an identity code to preserve the source identity. As for other variation factors, we consider them as native structures from the source image that should be reserved. Thus a dense correspondence field is integrated in the network to preserve the geometry on a face. To encourage delightful visual results after makeup transfer, we propose a cosmetic loss to learn makeup styles in a delicate way. Finally, a new CrossMakeup Face (CMF) benchmark dataset (https://github.com/ly-joy/Cross-Makeup-Face) with in-the-wild makeup portraits is built up to push the frontiers of related research. Both visual and quantitative experimental results on four makeup datasets demonstrate the superiority of the proposed method. |
关键词 | Face verification Makeup transfer Disentangled feature Correspondence field |
语种 | 英语 |
七大方向——子方向分类 | 图像视频处理与分析 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39152 |
专题 | 模式识别实验室 |
通讯作者 | He, Ran |
作者单位 | 1.Center for Research on Intelligent Perception and Computing, CASIA 2.National Laboratory of Pattern Recognition, CASIA 3.Center for Excellence in Brain Science and Intelligence 4.University of Chinese Academy of Sciences |
第一作者单位 | 模式识别国家重点实验室; 中国科学院自动化研究所 |
通讯作者单位 | 模式识别国家重点实验室; 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Li, Yi,Huang, Huaibo,Cao, Jie,et al. Disentangled Representation Learning of Makeup Portraits in the Wild[J]. International Journal of Computer Vision,2019,-(-):-. |
APA | Li, Yi,Huang, Huaibo,Cao, Jie,He, Ran,&Tan, Tieniu.(2019).Disentangled Representation Learning of Makeup Portraits in the Wild.International Journal of Computer Vision,-(-),-. |
MLA | Li, Yi,et al."Disentangled Representation Learning of Makeup Portraits in the Wild".International Journal of Computer Vision -.-(2019):-. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
201912_IJCV_MUP-D.pd(24456KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论