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Two-Stream Convolutional Networks for Dynamic Texture Synthesis

Dynamic texture synthesis

Requirements

Setup

  1. Store the appearance-stream tfmodel in ./models.
  2. Store the dynamics-stream tfmodel in ./models. The filepath to this model is your --dynamics_model path.

Dynamic texture synthesis

python synthesize.py --type=dts --gpu=<NUMBER> --runid=<NAME> --dynamics_target=data/dynamic_textures/<FOLDER> --dynamics_model=models/<TFMODEL>

Store your chosen dynamic texture image sequence in a folder in /data/dynamic_textures. This folder is your --dynamics_target path.

Example usage

python synthesize.py --type=dts --gpu=0 --runid="my_cool_fish" --dynamics_target=data/dynamic_textures/fish --dynamics_model=models/MSOEnet_ucf101train01_6e-4_allaug_exceptscale_randorder.tfmodel

Dynamics style transfer

python synthesize.py --type=dst --gpu=<NUMBER> --runid=<NAME> --dynamics_target=data/dynamic_textures/<FOLDER> --dynamics_model=models/<TFMODEL> --appearance_target=data/textures/<IMAGE>

Store your chosen static texture in ./data/textures. The filepath to this texture is your --appearance_target path.

Example usage

python synthesize.py --type=dst --gpu=0 --runid="whoa_water!" --dynamics_target=data/dynamic_textures/water_4 --appearance_target=data/textures/water_paint_cropped.jpeg --dynamics_model=models/MSOEnet_ucf101train01_6e-4_allaug_exceptscale_randorder.tfmodel

Temporally-endless dynamic texture synthesis

python synthesize.py --type=inf --gpu=<NUMBER> --runid=<NAME> --dynamics_target=data/dynamic_textures/<FOLDER> --dynamics_model=models/<TFMODEL>

Incremental dynamic texture synthesis

python synthesize.py --type=inc --gpu=<NUMBER> --runid=<NAME> --dynamics_target=data/dynamic_textures/<FOLDER> --dynamics_model=models/<TFMODEL> --appearance_target=data/textures/<IMAGE>

Store your chosen static texture in /data/textures. The filepath to this texture is your --appearance_target path. This texture should be the last frame of a previously generated sequence.

Static texture synthesis

python synthesize.py --type=sta --gpu=<NUMBER> --runid=<NAME> --appearance_target=data/textures/<IMAGE>

Gatys et al.'s method of texture synthesis.

Notes

The network's output is saved at data/out/<RUNID>.

Use ./useful_scripts/makegif.sh to create a gif from a folder of images, e.g.,

./useful_scripts/makegif.sh "data/out/calm_water/iter_6000*" calm_water.gif

will create the gif calm_water.gif from the images iter_6000* in the calm_water output folder.

Logs and snapshots are created and stored in ./logs/<RUNID> and ./snapshots/<RUNID>, respectively. You can view the loss progress for a particular run in Tensorboard.

Citation

@inproceedings{tesfaldet2018,
  author = {Matthew Tesfaldet and Marcus A. Brubaker and Konstantinos G. Derpanis},
  title = {Two-Stream Convolutional Networks for Dynamic Texture Synthesis},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018}
}

License

Two-Stream Convolutional Networks for Dynamic Texture Synthesis Copyright (C) 2018 Matthew Tesfaldet

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

For questions, please contact Matthew Tesfaldet (mtesfald@eecs.yorku.ca).