# Deep Video Color Propagation

arXiv: https://arxiv.org/abs/1808.03232 · HTML: https://mlsanity.com/paper/1808.03232/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2018.

## Built on by (most cited first, 21 of 21)

- [Many-to-many Splatting for Efficient Video Frame Interpolation](https://mlsanity.com/paper/2204.03513/) (2022)
- [A survey of synthetic data augmentation methods in computer vision](https://mlsanity.com/paper/2403.10075/) (2024)
- [The Animation Transformer: Visual Correspondence via Segment Matching](https://mlsanity.com/paper/2109.02614/) (2021)
- [Splatting-based Synthesis for Video Frame Interpolation](https://mlsanity.com/paper/2201.10075/) (2022)
- [Deep Video Prior for Video Consistency and Propagation](https://mlsanity.com/paper/2201.11632/) (2022)
- [A Perceptual Quality Metric for Video Frame Interpolation](https://mlsanity.com/paper/2210.01879/) (2022)
- [Cross-Attention Transformer for Video Interpolation](https://mlsanity.com/paper/2207.04132/) (2022)
- [ChromaDistill: Colorizing Monochrome Radiance Fields with Knowledge Distillation](https://mlsanity.com/paper/2309.07668/) (2023)
- [Benchmarking Video Frame Interpolation](https://mlsanity.com/paper/2403.17128/) (2024)
- [Unsupervised Flow Refinement near Motion Boundaries](https://mlsanity.com/paper/2208.02305/) (2022)
- [SVCNet: Scribble-based Video Colorization Network with Temporal Aggregation](https://mlsanity.com/paper/2303.11591/) (2023)
- [Video Frame Interpolation with Many-to-many Splatting and Spatial Selective Refinement](https://mlsanity.com/paper/2310.18946/) (2023)
- [IReNe: Instant Recoloring of Neural Radiance Fields](https://mlsanity.com/paper/2405.19876/) (2024)
- [Two Decades of Colorization and Decolorization for Images and Videos](https://mlsanity.com/paper/2204.13322/) (2022)
- [NeuriCam: Key-Frame Video Super-Resolution and Colorization for IoT Cameras](https://mlsanity.com/paper/2207.12496/) (2022)
- [Efficient Feature Extraction for High-resolution Video Frame Interpolation](https://mlsanity.com/paper/2211.14005/) (2022)
- [MultiCOIN: Multi-Modal COntrollable Video INbetweening](https://mlsanity.com/paper/2510.08561/) (2025)
- [VIRGi: View-dependent Instant Recoloring of 3D Gaussians Splats](https://mlsanity.com/paper/2603.02986/) (2026)
- [Learning Multimodal Affinities for Textual Editing in Images](https://mlsanity.com/paper/2103.10139/) (2021)
- [Towards Unified Keyframe Propagation Models](https://mlsanity.com/paper/2205.09731/) (2022)
- [Computer-aided Colorization State-of-the-science: A Survey](https://mlsanity.com/paper/2410.02288/) (2024)

## Abstract (arXiv)

Traditional approaches for color propagation in videos rely on some form of matching between consecutive video frames. Using appearance descriptors, colors are then propagated both spatially and temporally. These methods, however, are computationally expensive and do not take advantage of semantic information of the scene. In this work we propose a deep learning framework for color propagation that combines a local strategy, to propagate colors frame-by-frame ensuring temporal stability, and a global strategy, using semantics for color propagation within a longer range. Our evaluation shows the superiority of our strategy over existing video and image color propagation methods as well as neural photo-realistic style transfer approaches.