  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.
