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Improving Video Generation for Multi-functional Applications

GitHub repository for "Improving Video Generation for Multi-functional Applications"

Paper Link

For more information please refer to our homepage.

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 64x64 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 64, 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