# User-Guided Deep Anime Line Art Colorization with Conditional Adversarial Networks

arXiv: https://arxiv.org/abs/1808.03240 · HTML: https://mlsanity.com/paper/1808.03240/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2018.

## Built on by (most cited first, 30 of 32)

- [Colorization Transformer](https://mlsanity.com/paper/2102.04432/) (2021)
- [IMAGGarment: Fine-Grained Garment Generation for Controllable Fashion Design](https://mlsanity.com/paper/2504.13176/) (2025)
- [LVCD: Reference-based Lineart Video Colorization with Diffusion Models](https://mlsanity.com/paper/2409.12960/) (2024)
- [AniDoc: Animation Creation Made Easier](https://mlsanity.com/paper/2412.14173/) (2024)
- [The Animation Transformer: Visual Correspondence via Segment Matching](https://mlsanity.com/paper/2109.02614/) (2021)
- [APISR: Anime Production Inspired Real-World Anime Super-Resolution](https://mlsanity.com/paper/2403.01598/) (2024)
- [Semantic-Sparse Colorization Network for Deep Exemplar-based Colorization](https://mlsanity.com/paper/2112.01335/) (2021)
- [Mimetic Models: Ethical Implications of AI that Acts Like You](https://mlsanity.com/paper/2207.09394/) (2022)
- [AnimeDiffusion: Anime Face Line Drawing Colorization via Diffusion Models](https://mlsanity.com/paper/2303.11137/) (2023)
- [Focusing on Persons: Colorizing Old Images Learning from Modern Historical Movies](https://mlsanity.com/paper/2108.06515/) (2021)
- [ColorFlow: Retrieval-Augmented Image Sequence Colorization](https://mlsanity.com/paper/2412.11815/) (2024)
- [Cobra: Efficient Line Art COlorization with BRoAder References](https://mlsanity.com/paper/2504.12240/) (2025)
- [Attention-Aware Anime Line Drawing Colorization](https://mlsanity.com/paper/2212.10988/) (2022)
- [Semi-supervised reference-based sketch extraction using a contrastive learning framework](https://mlsanity.com/paper/2407.14026/) (2024)
- [Graph Jigsaw Learning for Cartoon Face Recognition](https://mlsanity.com/paper/2107.06532/) (2021)
- [Representative Feature Extraction During Diffusion Process for Sketch Extraction with One Example](https://mlsanity.com/paper/2401.04362/) (2024)
- [Guiding Users to Where to Give Color Hints for Efficient Interactive Sketch Colorization via Unsupervised Region Prioritization](https://mlsanity.com/paper/2210.14270/) (2022)
- [Improving Sketch Colorization using Adversarial Segmentation Consistency](https://mlsanity.com/paper/2301.08590/) (2023)
- [SVCNet: Scribble-based Video Colorization Network with Temporal Aggregation](https://mlsanity.com/paper/2303.11591/) (2023)
- [Re:Draw -- Context Aware Translation as a Controllable Method for Artistic Production](https://mlsanity.com/paper/2401.03499/) (2024)
- [Adversarial Segmentation Loss for Sketch Colorization](https://mlsanity.com/paper/2102.06192/) (2021)
- [Attention-based Stylisation for Exemplar Image Colourisation](https://mlsanity.com/paper/2105.01705/) (2021)
- [Generative Probabilistic Image Colorization](https://mlsanity.com/paper/2109.14518/) (2021)
- [XCI-Sketch: Extraction of Color Information from Images for Generation of Colored Outlines and Sketches](https://mlsanity.com/paper/2108.11554/) (2021)
- [Line Art Colorization of Fakemon using Generative Adversarial Neural Networks](https://mlsanity.com/paper/2307.05760/) (2023)
- [inkn'hue: Enhancing Manga Colorization from Multiple Priors with Alignment Multi-Encoder VAE](https://mlsanity.com/paper/2311.01804/) (2023)
- [Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey](https://mlsanity.com/paper/2412.01450/) (2024)
- [Audio-Infused Automatic Image Colorization by Exploiting Audio Scene Semantics](https://mlsanity.com/paper/2401.13270/) (2024)
- [Computer-aided Colorization State-of-the-science: A Survey](https://mlsanity.com/paper/2410.02288/) (2024)
- [SSIMBaD: Sigma Scaling with SSIM-Guided Balanced Diffusion for AnimeFace Colorization](https://mlsanity.com/paper/2506.04283/) (2025)

## Abstract (arXiv)

Scribble colors based line art colorization is a challenging computer vision problem since neither greyscale values nor semantic information is presented in line arts, and the lack of authentic illustration-line art training pairs also increases difficulty of model generalization. Recently, several Generative Adversarial Nets (GANs) based methods have achieved great success. They can generate colorized illustrations conditioned on given line art and color hints. However, these methods fail to capture the authentic illustration distributions and are hence perceptually unsatisfying in the sense that they often lack accurate shading. To address these challenges, we propose a novel deep conditional adversarial architecture for scribble based anime line art colorization. Specifically, we integrate the conditional framework with WGAN-GP criteria as well as the perceptual loss to enable us to robustly train a deep network that makes the synthesized images more natural and real. We also introduce a local features network that is independent of synthetic data. With GANs conditioned on features from such network, we notably increase the generalization capability over "in the wild" line arts. Furthermore, we collect two datasets that provide high-quality colorful illustrations and authentic line arts for training and benchmarking. With the proposed model trained on our illustration dataset, we demonstrate that images synthesized by the presented approach are considerably more realistic and precise than alternative approaches.