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We present the first generative adversarial network (GAN) for natural imagematting. Our novel generator network is trained to predict visually appealingalphas with the addition of the adversarial loss from the discriminator that istrained to classify well-composited images. Further, we improve existingencoder-decoder architectures to better deal with the spatial localizationissues inherited in convolutional neural networks (CNN) by using dilatedconvolutions to capture global context information without downscaling featuremaps and losing spatial information. We present state-of-the-art results on thealphamatting online benchmark for the gradient error and give comparableresults in others. Our method is particularly well suited for fine structureslike hair, which is of great importance in practical matting applications, e.g.in film/TV production.

AlphaGAN: Generative adversarial networks for natural image matting