# Automatic Video Colorization using 3D Conditional Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1905.03023 · HTML: https://mlsanity.com/paper/1905.03023/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2019.

## Built on by (most cited first, 5 of 5)

- [VCGAN: Video Colorization with Hybrid Generative Adversarial Network](https://mlsanity.com/paper/2104.12357/) (2021)
- [Exemplar-based Video Colorization with Long-term Spatiotemporal Dependency](https://mlsanity.com/paper/2303.15081/) (2023)
- [SVCNet: Scribble-based Video Colorization Network with Temporal Aggregation](https://mlsanity.com/paper/2303.11591/) (2023)
- [Temporal Consistent Automatic Video Colorization via Semantic Correspondence](https://mlsanity.com/paper/2305.07904/) (2023)
- [LatentColorization: Latent Diffusion-Based Speaker Video Colorization](https://mlsanity.com/paper/2405.05707/) (2024)

## Abstract (arXiv)

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.