Makeup Transfer妆容迁移是指将目标图上的妆容直接迁移到原图上的技术。相比传统贴妆技术,妆容迁移具有极高的自由度,它可以让用户不再局限于设计师设计好的妆容,而是可以自主、任意地从真实模特图中获取妆容,极大地丰富了妆容的多样性。此外,妆容迁移技术不仅可以迁移五官妆容信息,还可以对肤色、光影等信息进行整体迁移。目前,基于生成对抗网络的模型BeautyGAN和PSGAN已经在该领域取得了较好的效果。
Wentao Jiang,Si Liu, Chen Gao, Jie Cao,Ran He,Jiashi Feng,Shuicheng Yan
CVPR, pp.5193-5201, (2020)
We address the makeup transfer task, which aims to transfer the makeup from a reference image to a source image
Cited by16BibtexViews395DOI
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CVPR, pp.5729-5739, (2020)
To tackle the data problem and achieve makeup-invariant face recognition, we propose a unified face morphological multi-branch network, which includes two modules: face morphology network, and attention-based multi-branch network
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Yi Li,Huaibo Huang, Junchi Yu,Ran He,Tieniu Tan
arXiv preprint arXiv:2004.09147, (2020)
Face verification aims at determining whether a pair of face images belongs to the same identity. Recent studies have revealed the negative impact of facial makeup on the verification performance. With the rapid development of deep generative models, this paper proposes a semanti...
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Daichi Horita, Kiyoharu Aizawa
There are five features to consider when using generative adversarial networks to apply makeup to photos of the human face. These features include (1) facial components, (2) interactive color adjustments, (3) makeup variations, (4) robustness to poses and expressions, and the (...
Cited by0BibtexViews48
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Robin Kips,Pietro Gori,Matthieu Perrot, Isabelle Bloch
ECCV Workshops, pp.280-296, (2020)
While existing makeup style transfer models perform an image synthesis whose results cannot be explicitly controlled, the ability to modify makeup color continuously is a desirable property for virtual try-on applications. We propose a new formulation for the makeup style trans...
Cited by0BibtexViews32DOI
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Sun Zhaoyang, Liu Wenxuan, Liu Feng,Liu Ryan Wen, Xiong Shengwu
asian conference on computer vision, (2020)
Facial makeup transfer aims to render a non-makeup face image in an arbitrary given makeup one while preserving face identity. The most advanced method separates makeup style information from face images to realize makeup transfer. However, makeup style includes several semanti...
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Qiao Gu, Guanzhi Wang, Mang Tik Chiu,Yu-Wing Tai,Chi-Keung Tang
ICCV, pp.10480-10489, (2019)
We propose a local adversarial disentangling network (LADN) for facial makeup and de-makeup. Central to our method are multiple and overlapping local adversarial discriminators in a content-style disentangling network for achieving local detail transfer between facial images, w...
Cited by5BibtexViews56DOI
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Sarfraz M. Saquib, Seibold Constantin, Khalid Haroon,Stiefelhagen Rainer
BMVC, pp.287, (2019)
Generative adversarial networks has emerged as a defacto standard for image translation problems. To successfully drive such models, one has to rely on additional networks e.g., discriminators and/or perceptual networks. Training these networks with pixel based losses alone are...
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Liu Xudong, Wang Ruizhe, Chen Chih-Fan, Yin Minglei, Peng Hao, Ng Shukhan, Li Xin
Facial appearance plays an important role in our social lives. Subjective perception of women's beauty depends on various face-related (e.g., skin, shape, hair) and environmental (e.g., makeup, lighting, angle) factors. Similar to cosmetic surgery in the physical world, virtual...
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Zhang Honglun, Chen Wenqing,He Hao,Jin Yaohui
Facial makeup transfer is a widely-used technology that aims to transfer the makeup style from a reference face image to a non-makeup face. Existing literature leverage the adversarial loss so that the generated faces are of high quality and realistic as real ones, but are only...
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CVPR, pp.40-48, (2018)
This paper introduces an automatic method for editing a portrait photo so that the subject appears to be wearing makeup in the style of another person in a reference photo. Our unsupervised learning approach relies on a new framework of cycle-consistent generative adversarial net...
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MM '18: ACM Multimedia Conference Seoul Republic of Korea October, 2018, pp.645-653, (2018)
Facial makeup transfer aims to translate the makeup style from a given reference makeup face image to another non-makeup one while preserving face identity. Such an instance-level transfer problem is more challenging than conventional domain-level transfer tasks, especially when ...
Cited by75BibtexViews180DOI
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Yi Li, Lingxiao Song,Xiang Wu,Ran He,Tieniu Tan
national conference on artificial intelligence, (2018)
Makeup is widely used to improve facial attractiveness and is well accepted by the public. However, different makeup styles will result in significant facial appearance changes. It remains a challenging problem to match makeup and non-makeup face images. This paper proposes a lea...
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THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, pp.941-947, (2017)
A deep neural network based makeup recommendation model is trained from examples and knowledge base rules jointly
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Si Liu, Xinyu Ou, Ruihe Qian,Wei Wang,Xiaochun Cao
IJCAI, (2016): 2568-2575
In this paper, we propose a novel Deep Localized Makeup Transfer Network to automatically recommend the most suitable makeup for a female and synthesis the makeup on her face. Given a before-makeup face, her most suitable makeup is determined automatically. Then, both the before-...
Cited by69BibtexViews59
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