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This repository provides the pretrained model used in our Video2GIF work.
For information on how to use the code, please see our Tutorial.
In order to run the tutorial, you will first need to install the video2gif package by running
python setup.py install --user
in the code directory.
If you don't have cuDNN > 4.0 available, you need to use the Lasagne fork that provides 3D convolutions and pooling implementations without cuDNN. You can get it from https://github.com/gyglim/Lasagne
Note: In case the data is not available on the ETH links, where's a backup on drive:
C3D weights: https://drive.google.com/file/d/12J5Rsz3zBp3gKKvrmRU3B8s9OTAcfR7C/
Snippet mean: https://drive.google.com/file/d/1oKN5APlKzf70X4l4K4CIcuOUfbUCdHUt/
Video2GIF weights: https://drive.google.com/file/d/1Qvd6xXucwiis0bqlcoHB6pMIh7GXgdvK/
Example video: https://drive.google.com/file/d/17LdWCqUBy0-4uXLxbw9RSUH_fGTQAbVQ/
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If you end up using the code, we ask you to cite the following paper:
Michael Gygli, Yale Song, Liangliang Cao
"Video2GIF: Automatic Generation of Animated GIFs from Video,"
IEEE CVPR 2016
If you have any question regarding the dataset, please contact:
Michael Gygli <gygli@vision.ee.ethz.ch>
License: This code is licensed under GPL version 3, see LICENSE file
Note: There is a patent pending on the ideas presented in this work so this code should only be used for academic purposes.
Last edit: June 21, 2016