  This paper studies a combination of generative Markov random field (MRF)
models and discriminatively trained deep convolutional neural networks (dCNNs)
for synthesizing 2D images. The generative MRF acts on higher-levels of a dCNN
feature pyramid, controling the image layout at an abstract level. We apply the
method to both photographic and non-photo-realistic (artwork) synthesis tasks.
The MRF regularizer prevents over-excitation artifacts and reduces implausible
feature mixtures common to previous dCNN inversion approaches, permitting
synthezing photographic content with increased visual plausibility. Unlike
standard MRF-based texture synthesis, the combined system can both match and
adapt local features with considerable variability, yielding results far out of
reach of classic generative MRF methods.
