# ChromaGAN: Adversarial Picture Colorization with Semantic Class Distribution

arXiv: https://arxiv.org/abs/1907.09837 · HTML: https://mlsanity.com/paper/1907.09837/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2019.

## Built on by (most cited first, 30 of 61)

- [Towards Vivid and Diverse Image Colorization with Generative Color Prior](https://mlsanity.com/paper/2108.08826/) (2021)
- [DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders](https://mlsanity.com/paper/2212.11613/) (2022)
- [BigColor: Colorization using a Generative Color Prior for Natural Images](https://mlsanity.com/paper/2207.09685/) (2022)
- [Control Color: Multimodal Diffusion-based Interactive Image Colorization](https://mlsanity.com/paper/2402.10855/) (2024)
- [Diffusing Colors: Image Colorization with Text Guided Diffusion](https://mlsanity.com/paper/2312.04145/) (2023)
- [GLEAN: Generative Latent Bank for Image Super-Resolution and Beyond](https://mlsanity.com/paper/2207.14812/) (2022)
- [Improved Diffusion-based Image Colorization via Piggybacked Models](https://mlsanity.com/paper/2304.11105/) (2023)
- [VCGAN: Video Colorization with Hybrid Generative Adversarial Network](https://mlsanity.com/paper/2104.12357/) (2021)
- [Deep Edge-Aware Interactive Colorization against Color-Bleeding Effects](https://mlsanity.com/paper/2107.01619/) (2021)
- [Semantic-Sparse Colorization Network for Deep Exemplar-based Colorization](https://mlsanity.com/paper/2112.01335/) (2021)
- [Video Colorization with Pre-trained Text-to-Image Diffusion Models](https://mlsanity.com/paper/2306.01732/) (2023)
- [Wavelet Transform-assisted Adaptive Generative Modeling for Colorization](https://mlsanity.com/paper/2107.04261/) (2021)
- [SCSNet: An Efficient Paradigm for Learning Simultaneously Image Colorization and Super-Resolution](https://mlsanity.com/paper/2201.04364/) (2022)
- [UniColor: A Unified Framework for Multi-Modal Colorization with Transformer](https://mlsanity.com/paper/2209.11223/) (2022)
- [Colorizing Monochromatic Radiance Fields](https://mlsanity.com/paper/2402.12184/) (2024)
- [Automatic Controllable Colorization via Imagination](https://mlsanity.com/paper/2404.05661/) (2024)
- [Focusing on Persons: Colorizing Old Images Learning from Modern Historical Movies](https://mlsanity.com/paper/2108.06515/) (2021)
- [MultiColor: Image Colorization by Learning from Multiple Color Spaces](https://mlsanity.com/paper/2408.04172/) (2024)
- [L-CAD: Language-based Colorization with Any-level Descriptions using Diffusion Priors](https://mlsanity.com/paper/2305.15217/) (2023)
- [ChromaDistill: Colorizing Monochrome Radiance Fields with Knowledge Distillation](https://mlsanity.com/paper/2309.07668/) (2023)
- [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)
- [SPColor: Semantic Prior Guided Exemplar-based Image Colorization](https://mlsanity.com/paper/2304.06255/) (2023)
- [Attention-based Stylisation for Exemplar Image Colourisation](https://mlsanity.com/paper/2105.01705/) (2021)
- [Hiding Behind Backdoors: Self-Obfuscation Against Generative Models](https://mlsanity.com/paper/2201.09774/) (2022)
- [Multimodal Semantic-Aware Automatic Colorization with Diffusion Prior](https://mlsanity.com/paper/2404.16678/) (2024)
- [From Fragment to One Piece: A Survey on AI-Driven Graphic Design](https://mlsanity.com/paper/2503.18641/) (2025)
- [Digital Staining with Knowledge Distillation: A Unified Framework for Unpaired and Paired-But-Misaligned Data](https://mlsanity.com/paper/2504.09899/) (2025)
- [TUCaN: Progressively Teaching Colourisation to Capsules](https://mlsanity.com/paper/2106.15176/) (2021)
- [Bias in Automated Image Colorization: Metrics and Error Types](https://mlsanity.com/paper/2202.08143/) (2022)

## Abstract (arXiv)

The colorization of grayscale images is an ill-posed problem, with multiple correct solutions. In this paper, we propose an adversarial learning colorization approach coupled with semantic information. A generative network is used to infer the chromaticity of a given grayscale image conditioned to semantic clues. This network is framed in an adversarial model that learns to colorize by incorporating perceptual and semantic understanding of color and class distributions. The model is trained via a fully self-supervised strategy. Qualitative and quantitative results show the capacity of the proposed method to colorize images in a realistic way achieving state-of-the-art results.