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}.
翻译:我们从完全不同的角度解决了错误的阿尔法交配问题。 有了输入的肖像,而不是估算相应的阿尔法配方, 我们聚焦于另一端, 基底加强这种输入, 以便让任何现有的配方模型能够容易地估算阿尔法配方。 这是通过探索 GAN 模型的潜在空间而实现的。 证明在潜藏空间中可以找到可解释的方向, 它们与语义图像转换相对应 。 我们进一步在阿尔法配方中探索这一属性 。 特别是, 我们将一个输入的肖像转换到StyleGAN 潜在代码中, 我们的目标是发现在潜在空间中是否有一个更符合参考配方模型的强化版本。 我们优化了在四个定制损失下的潜在空间的多尺度潜在矢量,确保了配方特性和对肖像的细微修改。 我们证明拟议的方法可以改进任意配方模型的真实肖像图像, 提高自动配方图像的性能。 此外, 我们利用StyleGAN 的基因属性, 并提议生成一个更高级的肖像数据, 能够进一步处理现有的制式GTal_ GT.