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Improving Video Generation for Multi-functional Applications ================================================================== GitHub repository for "Improving Video Generation for Multi-functional Applications" [Paper Link](https://arxiv.org/abs/1711.11453) For more information please refer to [our homepage](https://bernhard2202.github.io/ivgan/index.html). Requirements ------------ * Tensorflow 1.2.1 * Python 2.7 * ffmpeg Data Format ----------- Videos are stored as JPEGs of vertically stacked frames. Every frame needs to be at least ***x*** pixels; videos contain between 16 and 32 frames. For an example datasets see: http://carlvondrick.com/tinyvideo/#data Training -------- python main_train.py Important Parameters: * mode: one of 'generate', 'predict', 'bw2rgb', 'inpaint' depending on weather you want to generate videos, predict future frames, colorize videos or do inpainting. * batch_size: Recommended ***, for colorization use 32 for memory issues. * root_dir: root directory of dataset * index_file: must be in root_dir, containing a list of all training data clips; path relative to root_dir. * experiment_name: name of experiment * output_every: output loss to stdout and write to tensorboard summary every xx steps. * sample_every: generate a visual sample every xx steps. * save_model_very: save the model every xx steps. * recover_model: if true recover model and continue training

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