# Tag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss

arXiv: https://arxiv.org/abs/1908.05840 · HTML: https://mlsanity.com/paper/1908.05840/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2019.

## Built on by (most cited first, 30 of 35)

- [Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing](https://mlsanity.com/paper/2104.14754/) (2021)
- [Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized Images](https://mlsanity.com/paper/2206.08549/) (2022)
- [LVCD: Reference-based Lineart Video Colorization with Diffusion Models](https://mlsanity.com/paper/2409.12960/) (2024)
- [Eliminating Gradient Conflict in Reference-based Line-Art Colorization](https://mlsanity.com/paper/2207.06095/) (2022)
- [AniDoc: Animation Creation Made Easier](https://mlsanity.com/paper/2412.14173/) (2024)
- [MangaNinja: Line Art Colorization with Precise Reference Following](https://mlsanity.com/paper/2501.08332/) (2025)
- [Learning Inclusion Matching for Animation Paint Bucket Colorization](https://mlsanity.com/paper/2403.18342/) (2024)
- [Follow-Your-Color: Multi-Instance Sketch Colorization](https://mlsanity.com/paper/2503.16948/) (2025)
- [Feature Statistics Mixing Regularization for Generative Adversarial Networks](https://mlsanity.com/paper/2112.04120/) (2021)
- [AnimeDiffusion: Anime Face Line Drawing Colorization via Diffusion Models](https://mlsanity.com/paper/2303.11137/) (2023)
- [ColorizeDiffusion: Adjustable Sketch Colorization with Reference Image and Text](https://mlsanity.com/paper/2401.01456/) (2024)
- [Generator Knows What Discriminator Should Learn in Unconditional GANs](https://mlsanity.com/paper/2207.13320/) (2022)
- [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)
- [Image Referenced Sketch Colorization Based on Animation Creation Workflow](https://mlsanity.com/paper/2502.19937/) (2025)
- [AnimeColor: Reference-based Animation Colorization with Diffusion Transformers](https://mlsanity.com/paper/2507.20158/) (2025)
- [Representative Feature Extraction During Diffusion Process for Sketch Extraction with One Example](https://mlsanity.com/paper/2401.04362/) (2024)
- [Rethinking Super-Resolution as Text-Guided Details Generation](https://mlsanity.com/paper/2207.06604/) (2022)
- [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)
- [Re:Draw -- Context Aware Translation as a Controllable Method for Artistic Production](https://mlsanity.com/paper/2401.03499/) (2024)
- [Paint Bucket Colorization Using Anime Character Color Design Sheets](https://mlsanity.com/paper/2410.19424/) (2024)
- [Memory Efficient Patch-based Training for INR-based GANs](https://mlsanity.com/paper/2207.01395/) (2022)
- [Flexible Portrait Image Editing with Fine-Grained Control](https://mlsanity.com/paper/2204.01318/) (2022)
- [inkn'hue: Enhancing Manga Colorization from Multiple Priors with Alignment Multi-Encoder VAE](https://mlsanity.com/paper/2311.01804/) (2023)
- [SketchColour: Channel Concat Guided DiT-based Sketch-to-Colour Pipeline for 2D Animation](https://mlsanity.com/paper/2507.01586/) (2025)
- [OmniColor: A Unified Framework for Multi-modal Lineart Colorization](https://mlsanity.com/paper/2603.27531/) (2026)

## Abstract (arXiv)

Line art colorization is expensive and challenging to automate. A GAN approach is proposed, called Tag2Pix, of line art colorization which takes as input a grayscale line art and color tag information and produces a quality colored image. First, we present the Tag2Pix line art colorization dataset. A generator network is proposed which consists of convolutional layers to transform the input line art, a pre-trained semantic extraction network, and an encoder for input color information. The discriminator is based on an auxiliary classifier GAN to classify the tag information as well as genuineness. In addition, we propose a novel network structure called SECat, which makes the generator properly colorize even small features such as eyes, and also suggest a novel two-step training method where the generator and discriminator first learn the notion of object and shape and then, based on the learned notion, learn colorization, such as where and how to place which color. We present both quantitative and qualitative evaluations which prove the effectiveness of the proposed method.