# Automatic Temporally Coherent Video Colorization

arXiv: https://arxiv.org/abs/1904.09527 · HTML: https://mlsanity.com/paper/1904.09527/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2019.

## Built on by (most cited first, 14 of 14)

- [LVCD: Reference-based Lineart Video Colorization with Diffusion Models](https://mlsanity.com/paper/2409.12960/) (2024)
- [Temporally Consistent Video Colorization with Deep Feature Propagation and Self-regularization Learning](https://mlsanity.com/paper/2110.04562/) (2021)
- [AniDoc: Animation Creation Made Easier](https://mlsanity.com/paper/2412.14173/) (2024)
- [Sakuga-42M Dataset: Scaling Up Cartoon Research](https://mlsanity.com/paper/2405.07425/) (2024)
- [BiSTNet: Semantic Image Prior Guided Bidirectional Temporal Feature Fusion for Deep Exemplar-based Video Colorization](https://mlsanity.com/paper/2212.02268/) (2022)
- [VCGAN: Video Colorization with Hybrid Generative Adversarial Network](https://mlsanity.com/paper/2104.12357/) (2021)
- [ColorMNet: A Memory-based Deep Spatial-Temporal Feature Propagation Network for Video Colorization](https://mlsanity.com/paper/2404.06251/) (2024)
- [AnimeColor: Reference-based Animation Colorization with Diffusion Transformers](https://mlsanity.com/paper/2507.20158/) (2025)
- [Exemplar-based Video Colorization with Long-term Spatiotemporal Dependency](https://mlsanity.com/paper/2303.15081/) (2023)
- [HDRVideo-GAN: Deep Generative HDR Video Reconstruction](https://mlsanity.com/paper/2110.11795/) (2021)
- [Learning Data-Driven Vector-Quantized Degradation Model for Animation Video Super-Resolution](https://mlsanity.com/paper/2303.09826/) (2023)
- [Temporal Consistent Automatic Video Colorization via Semantic Correspondence](https://mlsanity.com/paper/2305.07904/) (2023)
- [L-C4: Language-Based Video Colorization for Creative and Consistent Color](https://mlsanity.com/paper/2410.04972/) (2024)
- [BlazeBVD: Make Scale-Time Equalization Great Again for Blind Video Deflickering](https://mlsanity.com/paper/2403.06243/) (2024)

## Abstract (arXiv)

Greyscale image colorization for applications in image restoration has seen significant improvements in recent years. Many of these techniques that use learning-based methods struggle to effectively colorize sparse inputs. With the consistent growth of the anime industry, the ability to colorize sparse input such as line art can reduce significant cost and redundant work for production studios by eliminating the in-between frame colorization process. Simply using existing methods yields inconsistent colors between related frames resulting in a flicker effect in the final video. In order to successfully automate key areas of large-scale anime production, the colorization of line arts must be temporally consistent between frames. This paper proposes a method to colorize line art frames in an adversarial setting, to create temporally coherent video of large anime by improving existing image to image translation methods. We show that by adding an extra condition to the generator and discriminator, we can effectively create temporally consistent video sequences from anime line arts. Code and models available at: https://github.com/Harry-Thasarathan/TCVC