# Automatic Video Colorization using 3D Conditional Generative Adversarial Networks

## TL;DR

This paper introduces a novel 3D conditional GAN approach for automatic colorization of grayscale videos, effectively utilizing spatiotemporal information to produce consistent and realistic color sequences, validated on old film datasets.

## Contribution

The paper proposes a 3D convolutional GAN for video colorization, incorporating temporal context and a new metric for colorization consistency, advancing beyond frame-by-frame methods.

## Key findings

- Successful colorization of old black-and-white films
- Effective use of 3D convolutions for temporal coherence
- Introduction of a new metric for colorization consistency

## Abstract

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.