Browsing by Author "Shen, Chunhua"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Effective Eyebrow Matting with Domain Adaptation
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Wang, Luyuan; Zhang, Hanyuan; Xiao, Qinjie; Xu, Hao; Shen, Chunhua; Jin, Xiaogang; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
    We 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.