O r i g i n a l ( g ) P a r a me t e r i z a o n o f ( f ) ( h ) E y e b r o w r e mo v a l ( i ) P a r a me t e r i z a o n o f ( h ) Figure 1: We create the first synthetic eyebrow matting dataset (a). This enables semi-supervised training of a domain adaptation eyebrow matting network. The network can learn domain-robust features from synthetic data (a) together with unlabeled real-world eyebrow images without using any real matting data and estimate high-quality eyebrow alpha matte (c) from a real RGB image (b) only without any prior.The eyebrow matting allows us to automatically remove the interference of eyebrows during the multi-view stereo (MVS) based 3D face reconstruction process, and therefore largely enhances the efficiency and efficacy of the reconstruction of eyebrow regions. Without eyebrow removal, the reconstructed eyebrow geometry (f) often induces noises and artifacts when fitting the eyebrow during 3D parametric face reconstruction (g), which requires very expensive manual repair in hours. In contrast, eyebrow matting facilitates the easy attainment of better geometry (h) and more faithful parameterization of the eyebrow region (i). Furthermore, our eyebrow matting method can be used for cosmetic design purposes such as eyebrow recoloring (d) and eyebrow replacement (e).
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