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The implementation of Temporal Generative Adversarial Nets with Singular Value Clipping
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Temporal Generative Adversarial Nets

This repository contains a collection of scripts used in the experiments of Temporal Generative Adversarial Nets with Singular Value Clipping.

Disclaimer: PFN provides no warranty or support for this implementation. Use it at your own risk. See license for details.



These scripts require the following python libraries.

  • Chainer 2.0.0+
  • h5py
  • numpy
  • pandas
  • PIL
  • PyYAML
  • matplotlib

Note that they also require ffmpeg to produce a video from a set of images.



In order to run our scripts, you need to prepare MovingMNIST and UCF-101 datasets as follows.


  1. Download mnist_test_seq.npy from here.
  2. Put it on path-to-tgans/data/mnist_test_seq.npy.


There are two ways to create an UCF-101 dataset for this script.

  1. Transforms all the videos in the UCF-101 dataset to the images.
  2. Resizes these images to the appropriate resolution, and concatenate them into as single hdf5 format represented as (time, channel, rows, cols). In this transformation we used in this repository. Note that this script also produces a config file that describes videos and these corresponding label information.
  3. puts them on path-to-tgans/data.

Another way is to simply download these files; please download them from this url, and put them on the same directory.


TGAN with WGAN and Singular Value Clipping

python --config_path configs/moving_mnist/mnist_wgan_svd_zdim-100_no-beta-all_init-uniform-all.yml --gpu 0
python --config_path configs/ucf101/ucf101_wgan_svd_zdim-100_no-beta.yml --gpu 0

TGAN (WGAN and weight clipping)

python --config_path configs/moving_mnist/mnist_wgan_clip_zdim-100_no-beta-all_init-uniform-all.yml --gpu 0
python --config_path configs/ucf101/ucf101_wgan_clip_zdim-100_no-beta.yml --gpu 0

TGAN (vanilla GAN)

python --config_path configs/ucf101/ucf101_vanilla_zdim-100_no-beta.yml --gpu 0


Please cite the paper if you are interested in:

    author = {Saito, Masaki and Matsumoto, Eiichi and Saito, Shunta},
    title = {Temporal Generative Adversarial Nets with Singular Value Clipping},
    booktitle = {ICCV},
    year = {2017},


MIT License. Please see the LICENSE file for details.

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