In this work, we present a method for automatic colorization of grayscale videos. The core of the method is a Generative Adversarial Network that is trained and tested on sequences of frames in a sliding window manner. Network convolutional and deconvolutional layers are three-dimensional, with frame height, width and time as the dimensions taken into account. Multiple chrominance estimates per frame are aggregated and combined with available luminance information to recreate a colored sequence. Colorization trials are run succesfully on a dataset of old black-and-white films. The usefulness of our method is also validated with numerical results, computed with a newly proposed metric that measures colorization consistency over a frame sequence.
@article{arxiv.1905.03023,
title = {Automatic Video Colorization using 3D Conditional Generative Adversarial Networks},
author = {Panagiotis Kouzouglidis and Giorgos Sfikas and Christophoros Nikou},
journal= {arXiv preprint arXiv:1905.03023},
year = {2019}
}