Painting Outside the Box: Image Outpainting with GANs
Published in
arXiv.org(2018)External Links:
TL
TL;DR
A deep learning approach based on Iizuka et al. for adversarially training a network to hallucinate past image boundaries to show that deep learning approaches to image outpainting are both feasible and promising.
Abstract
The challenging task of image outpainting (extrapolation) has received comparatively little attention in relation to its cousin, image inpainting (completion). Accordingly, we present a deep learning approach based on Iizuka et al. for adversarially training a network to hallucinate past image boundaries. We use a three-phase training schedule to stably train a DCGAN architecture on a subset of the Places365 dataset. In line with Iizuka et al., we also use local discriminators to enhance the quality of our output. Once trained, our model is able to outpaint $128 \times 128$ color images relatively realistically, thus allowing for recursive outpainting. Our results show that deep learning approaches to image outpainting are both feasible and promising.
References10 items
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Places: A 10 Million Image Database for Scene Recognition
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Image Inpainting for Irregular Holes Using Partial Convolutions
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Globally and locally consistent image completion
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Wasserstein GAN
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