Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks

@inproceedings{Li2016PrecomputedRT,
  title={Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks},
  author={Chuan Li and Michael Wand},
  booktitle={ECCV},
  year={2016}
}
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 feedforward, strided… CONTINUE READING

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