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README.md

README.md

Conditional Generative ConvNets for Exemplar-based Texture Synthesis

This is a tensorflow implement of the cgCNN model proposed in Conditional Generative ConvNets for Exemplar-based Texture Synthesis.

This repository contains implements of three tasks as described below.

Tasks Description
Texture synthesis Synthesize new textures that are visually similar to the given exemplar texture.
Texture expansion Synthesize new textures that are larger (or arbitrarily large) than the given exemplar texture.
Texture inpainting Fill the corrupted region in the given exemplar texture.

Three types of textures, i.e. dynamic, image and sound textures, are considered for each task, and each application is placed in an individual folder. For example, the code for dynamic texture inpainting is placed in ./dynamic_inpaint, and the code for image texture expansion is placed in ./image_expansion.

Please visit our project page for more results.

Requirements

  • python=3.5
  • tensorflow=1.8.0
  • keras=2.1.6
  • librosa (for audio loading and saving)
  • ffmpeg (for video loading and saving)

Results

Texture synthesis

Image texture synthesis

Exemplar Result

Dynamic texture synthesis

Exemplar Result

Sound texture synthesis

Exemplar Result
applause_exemplar applause_synthesis

Texture expansion

Image texture expansion

Exemplar Result

Dynamic texture expansion

Exemplar Result

Sound texture expansion

Exemplar Result
shaking_paper_exemplar shaking_paper_expansion

Texture inpainting

Image texture inpainting

Exemplar Result

Dynamic texture inpainting

Exemplar Result

Sound texture inpainting

Exemplar Result
bees_exemplar bees_inpainted

Reference

@misc{wang2019conditional,
title={Conditional Generative ConvNets for Exemplar-based Texture Synthesis},
author={Zi-Ming Wang and Meng-Han Li and Gui-Song Xia},
year={2019},
eprint={1912.07971},
archivePrefix={arXiv},
primaryClass={cs.CV}
}

For any question, please contact Ziming Wang (wangzm@whu.edu.cn).

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