Texture Networks: Feed-forward Synthesis of Textures and Stylized Images

Code

• github.com/zhanghang1989/PyTorch-Multi-Style-Transfer
  pytorch★ 0
• github.com/ryanwebster90/image-synthesis-lab2
  pytorch★ 0
• github.com/ryanwebster90/texture-synthesis-lab
  pytorch★ 0
• github.com/noufali/VideoML
  pytorch★ 0
• github.com/ProofByConstruction/texture-networks
  tf★ 0
• github.com/lxy5513/Multi-Style-Transfer
  pytorch★ 0
• github.com/repyevsky/texture-nets
  tf★ 0
• github.com/habout632/gans
  pytorch★ 0
• github.com/JorgeGtz/TextureNets_implementation
  pytorch★ 0

Abstract

Gatys et al. recently demonstrated that deep networks can generate beautiful textures and stylized images from a single texture example. However, their methods requires a slow and memory-consuming optimization process. We propose here an alternative approach that moves the computational burden to a learning stage. Given a single example of a texture, our approach trains compact feed-forward convolutional networks to generate multiple samples of the same texture of arbitrary size and to transfer artistic style from a given image to any other image. The resulting networks are remarkably light-weight and can generate textures of quality comparable to Gatys~et~al., but hundreds of times faster. More generally, our approach highlights the power and flexibility of generative feed-forward models trained with complex and expressive loss functions.

Tasks

Reproductions