  We resolve the ill-posed alpha matting problem from a completely different
perspective. Given an input portrait image, instead of estimating the
corresponding alpha matte, we focus on the other end, to subtly enhance this
input so that the alpha matte can be easily estimated by any existing matting
models. This is accomplished by exploring the latent space of GAN models. It is
demonstrated that interpretable directions can be found in the latent space and
they correspond to semantic image transformations. We further explore this
property in alpha matting. Particularly, we invert an input portrait into the
latent code of StyleGAN, and our aim is to discover whether there is an
enhanced version in the latent space which is more compatible with a reference
matting model. We optimize multi-scale latent vectors in the latent spaces
under four tailored losses, ensuring matting-specificity and subtle
modifications on the portrait. We demonstrate that the proposed method can
refine real portrait images for arbitrary matting models, boosting the
performance of automatic alpha matting by a large margin. In addition, we
leverage the generative property of StyleGAN, and propose to generate enhanced
portrait data which can be treated as the pseudo GT. It addresses the problem
of expensive alpha matte annotation, further augmenting the matting performance
of existing models. Code is available
at~\url{https://github.com/cnnlstm/StyleGAN_Matting}.
