This repository contains an implementation of Video Imagination from a Single Image with Transformation Generation. The framework can synthesize multiple imaginary video from a single image.
We randomly pick some imaginary videos synthesized by our framework. The input is a single image from UCF101 dataset, and the output imaginary video contains five frames. The following gif picture is a demo of synthesized imaginary video. The network may bring some delay, please wait a while fro the demonstration.
Imaginary Video
Input image
The framework can be trained on three datasets : moving MNIST, 2D shape, UCF101. No pre-process is needed except normalizing images to be in the range [0, 1]. The videos (or image tuples) needs to be convert to tfrecords at first.
The code requires a TensorFlowr r1.0 installation
To train the framework, after you prepare the tfrecords, run main.py. This file will build model and graph, and train the networks.
The code is modified based on A Tensorflow Implementation of DCGAN. The on-the-fly 2D shape dataset generating codes are modified from the author of the dataset.