Answers.Body: <p>Most of the algorithms (based on <a href="http://arxiv.org/abs/1603.01768" rel="nofollow">image synthesis and style transfer</a>, e.g. <a href="https://github.com/alexjc/neural-doodle" rel="nofollow">neural-doodle</a>) haven't been proven to be highly effective in terms of real-time image processing.</p>
  
  <p>However the following studies discusses such algorithms for real-time texture synthesis:</p>
  
  <ul>
  <li><p><a href="http://arxiv.org/abs/1603.03417" rel="nofollow">Feed-forward Synthesis of Textures and Stylized Images</a></p>
  
  <p>The approach is to move the computational burden to a learning stage, making trained network (<a href="https://en.wikipedia.org/wiki/Convolutional_neural_network" rel="nofollow">CNN</a>) light-weight and compact in order to generate multiple samples of the same texture. This can generate textures as good as comparable to <a href="http://arxiv.org/abs/1505.07376" rel="nofollow">Gatys~et~al</a>, but significantly faster.</p></li>
  <li><p><a href="http://arxiv.org/abs/1603.08155" rel="nofollow">Perceptual Losses for Real-Time Style Transfer and Super-Resolution</a></p>
  
  <p>This method uses parallel work which can generate high-quality images by defining and optimizing loss functions based on high-level features extracted from pretrained networks.</p></li>
  <li><p><a href="http://arxiv.org/abs/1604.04382" rel="nofollow">Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks</a></p>
  
  <p>This uses precomputed feed-forward networks that captures the feature statistics of <a href="http://www.irisa.fr/vista/Papers/2008_LNLA_Pecot.pdf" rel="nofollow">Markovian patches</a> in order to generate outputs of arbitrary dimensions. This can be applied to texture synthesis, style transfer and video stylization.</p></li>
  </ul>
  
  <p><sup>Source: Above list suggested on <a href="https://github.com/alexjc/neural-doodle" rel="nofollow">neural-doodle</a> project.</sup></p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2016-08-11T20:39:12.370Z
Answers.LastActivityDate: 2016-08-11T20:49:24.500Z
Answers.LastEditDate: 2016-08-11T20:49:24.500Z
Answers.UserDisplayName: kenorb
Body: <p>Ideally I'd like to watch movie which is deep dreamed in real-time. Most algorithms which I know are too slow or not designed for real-time processing.</p>
  
  <p>For example I'm bored with some movie which I've watched thousands of time and I'd like to add some "dreaming" to it which is real-time filter which takes input frames, then it's processing and enhances the images through artificial neural network to achieve doodled output.</p>
  
  <p>Doesn't have to be exactly <a href="https://en.wikipedia.org/wiki/DeepDream" rel="nofollow">DeepDream</a> or hallucinogenic technique (which could be too much to watch for 2h), but with any similar ANN algorithm. I'm more interested into achieving desired real-time use.</p>
  
  <p>What kind of techniques can achieve such efficiency?</p>
  
Tags: <research><algorithm><real-time><neural-doodle>
Title: Which techniques can achieve neural doodle in real-time?
UserDisplayName: kenorb
ThreadText: # Which techniques can achieve neural doodle in real-time?
  
  (Asked by: username_0 on 2016-08-11)
  
  Ideally I'd like to watch movie which is deep dreamed in real-time. Most algorithms which I know are too slow or not designed for real-time processing.
  
  For example I'm bored with some movie which I've watched thousands of time and I'd like to add some "dreaming" to it which is real-time filter which takes input frames, then it's processing and enhances the images through artificial neural network to achieve doodled output.
  
  Doesn't have to be exactly [DeepDream](https://en.wikipedia.org/wiki/DeepDream) or hallucinogenic technique (which could be too much to watch for 2h), but with any similar ANN algorithm. I'm more interested into achieving desired real-time use.
  
  What kind of techniques can achieve such efficiency?
  
  **Tags:** research, algorithm, real-time, neural-doodle
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_0 on 2016-08-11. Score: 1 | Accepted answer
  
  Most of the algorithms (based on [image synthesis and style transfer](http://arxiv.org/abs/1603.01768), e.g. [neural-doodle](https://github.com/alexjc/neural-doodle)) haven't been proven to be highly effective in terms of real-time image processing.
  
  However the following studies discusses such algorithms for real-time texture synthesis:
  
  * [Feed-forward Synthesis of Textures and Stylized Images](http://arxiv.org/abs/1603.03417)
  
    The approach is to move the computational burden to a learning stage, making trained network ([CNN](https://en.wikipedia.org/wiki/Convolutional_neural_network)) light-weight and compact in order to generate multiple samples of the same texture. This can generate textures as good as comparable to [Gatys~et~al](http://arxiv.org/abs/1505.07376), but significantly faster.
  * [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](http://arxiv.org/abs/1603.08155)
  
    This method uses parallel work which can generate high-quality images by defining and optimizing loss functions based on high-level features extracted from pretrained networks.
  * [Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks](http://arxiv.org/abs/1604.04382)
  
    This uses precomputed feed-forward networks that captures the feature statistics of [Markovian patches](http://www.irisa.fr/vista/Papers/2008_LNLA_Pecot.pdf) in order to generate outputs of arbitrary dimensions. This can be applied to texture synthesis, style transfer and video stylization.
  
  Source: Above list suggested on [neural-doodle](https://github.com/alexjc/neural-doodle) project.
  
  
  ---
  
  