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We present the first generative adversarial network (GAN) for natural image matting. Our novel generator network is trained to predict visually appealing alphas with the addition of the adversarial loss from the discriminator that is trained to classify wellcomposited images. Further, we improve existing encoder-decoder architectures to better deal with the spatial localization issues inherited in convolutional neural networks (CNN) by using dilated convolutions to capture global context information without downscaling feature maps and losing spatial information. We present state-of-the-art results on the alphamatting online benchmark for the gradient error and give comparable results in others. Our method is particularly well suited for fine structures like hair, which is of great importance in practical matting applications, e.g. in film/TV production. c

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This article describes an investigation of interactive narrative in virtual reality (VR) through Samuel Beckett's theatrical text Play. Actors are captured in a green screen environment using free-viewpoint video (FVV). Built in a game engine, the scene is complete with binaural spatial audio and six degrees of freedom of movement. The project explores how ludic qualities in the original text elicit the conversational and interactive specificities of the digital medium. The work affirms potential for interactive narrative in VR, opens new experiences of the text and highlights the reorganization of the author-audience dynamic.

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