# TET-GAN: Text Effects Transfer via Stylization and Destylization

arXiv: https://arxiv.org/abs/1812.06384 · HTML: https://mlsanity.com/paper/1812.06384/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2018.

## Built on by (most cited first, 19 of 19)

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- [Intelligent Artistic Typography: A Comprehensive Review of Artistic Text Design and Generation](https://mlsanity.com/paper/2407.14774/) (2024)
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- [Learning Implicit Glyph Shape Representation](https://mlsanity.com/paper/2106.08573/) (2021)
- [FASTER: A Font-Agnostic Scene Text Editing and Rendering Framework](https://mlsanity.com/paper/2308.02905/) (2023)
- [VQ-Font: Few-Shot Font Generation with Structure-Aware Enhancement and Quantization](https://mlsanity.com/paper/2308.14018/) (2023)
- [Dynamic Texture Transfer using PatchMatch and Transformers](https://mlsanity.com/paper/2402.00606/) (2024)
- [ChArtist: Generating Pictorial Charts with Unified Spatial and Subject Control](https://mlsanity.com/paper/2603.14209/) (2026)
- [FontCrafter: High-Fidelity Element-Driven Artistic Font Creation with Visual In-Context Generation](https://mlsanity.com/paper/2603.22054/) (2026)
- [GAS-NeXt: Few-Shot Cross-Lingual Font Generator](https://mlsanity.com/paper/2212.02886/) (2022)
- [Total Disentanglement of Font Images into Style and Character Class Features](https://mlsanity.com/paper/2403.12784/) (2024)
- [Text Image Generation for Low-Resource Languages with Dual Translation Learning](https://mlsanity.com/paper/2409.17747/) (2024)
- [WordCraft: Interactive Artistic Typography with Attention Awareness and Noise Blending](https://mlsanity.com/paper/2507.09573/) (2025)
- [InkDiffuser: High-Fidelity One-shot Chinese Calligraphy via Differentiable Morphological Optimization](https://mlsanity.com/paper/2605.05865/) (2026)
- [HoloTetSphere: Unified TetSphere Mesh Reconstruction for Physical Simulations](https://mlsanity.com/paper/2607.08398/) (2026)

## Abstract (arXiv)

Text effects transfer technology automatically makes the text dramatically more impressive. However, previous style transfer methods either study the model for general style, which cannot handle the highly-structured text effects along the glyph, or require manual design of subtle matching criteria for text effects. In this paper, we focus on the use of the powerful representation abilities of deep neural features for text effects transfer. For this purpose, we propose a novel Texture Effects Transfer GAN (TET-GAN), which consists of a stylization subnetwork and a destylization subnetwork. The key idea is to train our network to accomplish both the objective of style transfer and style removal, so that it can learn to disentangle and recombine the content and style features of text effects images. To support the training of our network, we propose a new text effects dataset with as much as 64 professionally designed styles on 837 characters. We show that the disentangled feature representations enable us to transfer or remove all these styles on arbitrary glyphs using one network. Furthermore, the flexible network design empowers TET-GAN to efficiently extend to a new text style via one-shot learning where only one example is required. We demonstrate the superiority of the proposed method in generating high-quality stylized text over the state-of-the-art methods.