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arXiv · 1603.03417

Texture Networks: Feed-forward Synthesis of Textures and Stylized Images

Dmitry Ulyanov·Vadim Lebedev·Andrea Vedaldi·Victor Lempitsky
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Abstract

Gatys et al. recently demonstrated that deep networks can generate beautiful textures and stylized images from a single texture example. However, their methods requires a slow and memory-consuming optimization process. We propose here an alternative approach that moves the computational burden to a learning stage. Given a single example of a texture, our approach trains compact feed-forward convolutional networks to generate multiple samples of the same texture of arbitrary size and to transfer artistic style from a given image to any other image. The resulting networks are remarkably light-weight and can generate textures of quality comparable to Gatys~et~al., but hundreds of times faster. More generally, our approach highlights the power and flexibility of generative feed-forward models trained with complex and expressive loss functions.

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Dmitry Ulyanov, Vadim Lebedev, Andrea Vedaldi, Victor Lempitsky. 2016-03-10. Texture Networks: Feed-forward Synthesis of Textures and Stylized Images. https://arxiv.org/abs/1603.03417

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