Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis

Chuan Li; Michael Wand

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arXiv · 1601.04589

Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis

Chuan Li·Michael Wand

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Abstract

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.

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BibTeX RIS

Chuan Li, Michael Wand. 2016-01-18. Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis. https://arxiv.org/abs/1601.04589

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