  This paper proposes Markovian Generative Adversarial Networks (MGANs), a
method for training generative neural networks for efficient texture synthesis.
While deep neural network approaches have recently demonstrated remarkable
results in terms of synthesis quality, they still come at considerable
computational costs (minutes of run-time for low-res images). Our paper
addresses this efficiency issue. Instead of a numerical deconvolution in
previous work, we precompute a feed-forward, strided convolutional network that
captures the feature statistics of Markovian patches and is able to directly
generate outputs of arbitrary dimensions. Such network can directly decode
brown noise to realistic texture, or photos to artistic paintings. With
adversarial training, we obtain quality comparable to recent neural texture
synthesis methods. As no optimization is required any longer at generation
time, our run-time performance (0.25M pixel images at 25Hz) surpasses previous
neural texture synthesizers by a significant margin (at least 500 times
faster). We apply this idea to texture synthesis, style transfer, and video
stylization.
