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dc.contributor.authorWang, Luyuanen_US
dc.contributor.authorZhang, Hanyuanen_US
dc.contributor.authorXiao, Qinjieen_US
dc.contributor.authorXu, Haoen_US
dc.contributor.authorShen, Chunhuaen_US
dc.contributor.authorJin, Xiaogangen_US
dc.contributor.editorUmetani, Nobuyukien_US
dc.contributor.editorWojtan, Chrisen_US
dc.contributor.editorVouga, Etienneen_US
dc.date.accessioned2022-10-04T06:41:21Z
dc.date.available2022-10-04T06:41:21Z
dc.date.issued2022
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.14682
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14682
dc.description.abstractWe present the first synthetic eyebrow matting datasets and a domain adaptation eyebrow matting network for learning domain-robust feature representation using synthetic eyebrow matting data and unlabeled in-the-wild images with adversarial learning. Different from existing matting methods that may suffer from the lack of ground-truth matting datasets, which are typically labor-intensive to annotate or even worse, unable to obtain, we train the matting network in a semi-supervised manner using synthetic matting datasets instead of ground-truth matting data while achieving high-quality results. Specifically, we first generate a large-scale synthetic eyebrow matting dataset by rendering avatars and collect a real-world eyebrow image dataset while maximizing the data diversity as much as possible. Then, we use the synthetic eyebrow dataset to train a multi-task network, which consists of a regression task to estimate the eyebrow alpha mattes and an adversarial task to adapt the learned features from synthetic data to real data. As a result, our method can successfully train an eyebrow matting network using synthetic data without the need to label any real data. Our method can accurately extract eyebrow alpha mattes from in-the-wild images without any additional prior and achieves state-of-the-art eyebrow matting performance. Extensive experiments demonstrate the superior performance of our method with both qualitative and quantitative results.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies --> Image processing
dc.subjectComputing methodologies
dc.subjectImage processing
dc.titleEffective Eyebrow Matting with Domain Adaptationen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersImage Detection and Understanding
dc.description.volume41
dc.description.number7
dc.identifier.doi10.1111/cgf.14682
dc.identifier.pages347-358
dc.identifier.pages12 pages


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  • 41-Issue 7
    Pacific Graphics 2022 - Symposium Proceedings

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