# Aesthetic Text Logo Synthesis via Content-aware Layout Inferring

arXiv: https://arxiv.org/abs/2204.02701 · HTML: https://mlsanity.com/paper/2204.02701/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 39 references that are in the map.
- First posted on arXiv 6 Apr 2022.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Generative Adversarial Text to Image Synthesis](https://mlsanity.com/paper/1605.05396/) (2016)
- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [Generating Videos with Scene Dynamics](https://mlsanity.com/paper/1609.02612/) (2016)
- [DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks](https://mlsanity.com/paper/1711.07064/) (2017)
- [Gated Fusion Network for Single Image Dehazing](https://mlsanity.com/paper/1804.00213/) (2018)
- [LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators](https://mlsanity.com/paper/1901.06767/) (2019)
- [LayoutTransformer: Layout Generation and Completion with Self-attention](https://mlsanity.com/paper/2006.14615/) (2020)
- [LayoutVAE: Stochastic Scene Layout Generation From a Label Set](https://mlsanity.com/paper/1907.10719/) (2019)
- [Invertible Conditional GANs for image editing](https://mlsanity.com/paper/1611.06355/) (2016)
- [Variational Transformer Networks for Layout Generation](https://mlsanity.com/paper/2104.02416/) (2021)
- [Neural Design Network: Graphic Layout Generation with Constraints](https://mlsanity.com/paper/1912.09421/) (2019)
- [Dual Motion GAN for Future-Flow Embedded Video Prediction](https://mlsanity.com/paper/1708.00284/) (2017)
- [Attribute-conditioned Layout GAN for Automatic Graphic Design](https://mlsanity.com/paper/2009.05284/) (2020)
- [Separating Style and Content for Generalized Style Transfer](https://mlsanity.com/paper/1711.06454/) (2017)
- [Artistic Glyph Image Synthesis via One-Stage Few-Shot Learning](https://mlsanity.com/paper/1910.04987/) (2019)
- [Analogical Reasoning on Chinese Morphological and Semantic Relations](https://mlsanity.com/paper/1805.06504/) (2018)
- [Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach](https://mlsanity.com/paper/2011.14021/) (2020)
- [Awesome Typography: Statistics-Based Text Effects Transfer](https://mlsanity.com/paper/1611.09026/) (2016)
- [Attribute2Font: Creating Fonts You Want From Attributes](https://mlsanity.com/paper/2005.07865/) (2020)
- [Discovering Pattern Structure Using Differentiable Compositing](https://mlsanity.com/paper/2010.08788/) (2020)

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

- [SVGDreamer: Text Guided SVG Generation with Diffusion Model](https://mlsanity.com/paper/2312.16476/) (2023)
- [Word-As-Image for Semantic Typography](https://mlsanity.com/paper/2303.01818/) (2023)
- [LayoutDM: Transformer-based Diffusion Model for Layout Generation](https://mlsanity.com/paper/2305.02567/) (2023)
- [PRIOR: Prototype Representation Joint Learning from Medical Images and Reports](https://mlsanity.com/paper/2307.12577/) (2023)
- [AutoPoster: A Highly Automatic and Content-aware Design System for Advertising Poster Generation](https://mlsanity.com/paper/2308.01095/) (2023)
- [StrokeNUWA: Tokenizing Strokes for Vector Graphic Synthesis](https://mlsanity.com/paper/2401.17093/) (2024)
- [Desigen: A Pipeline for Controllable Design Template Generation](https://mlsanity.com/paper/2403.09093/) (2024)
- [CreatiPoster: Towards Editable and Controllable Multi-Layer Graphic Design Generation](https://mlsanity.com/paper/2506.10890/) (2025)
- [LayoutDETR: Detection Transformer Is a Good Multimodal Layout Designer](https://mlsanity.com/paper/2212.09877/) (2022)
- [Towards Diverse and Consistent Typography Generation](https://mlsanity.com/paper/2309.02099/) (2023)
- [TypeDance: Creating Semantic Typographic Logos from Image through Personalized Generation](https://mlsanity.com/paper/2401.11094/) (2024)
- [Kinetic Typography Diffusion Model](https://mlsanity.com/paper/2407.10476/) (2024)
- [Intelligent Artistic Typography: A Comprehensive Review of Artistic Text Design and Generation](https://mlsanity.com/paper/2407.14774/) (2024)
- [GLDesigner: Leveraging Multi-Modal LLMs as Designer for Enhanced Aesthetic Text Glyph Layouts](https://mlsanity.com/paper/2411.11435/) (2024)
- [AmbiGen: Generating Ambigrams from Pre-trained Diffusion Model](https://mlsanity.com/paper/2312.02967/) (2023)
- [What Text Design Characterizes Book Genres?](https://mlsanity.com/paper/2402.16356/) (2024)
- [From Fragment to One Piece: A Survey on AI-Driven Graphic Design](https://mlsanity.com/paper/2503.18641/) (2025)
- [StrokeGAN+: Few-Shot Semi-Supervised Chinese Font Generation with Stroke Encoding](https://mlsanity.com/paper/2211.06198/) (2022)
- [SGCE-Font: Skeleton Guided Channel Expansion for Chinese Font Generation](https://mlsanity.com/paper/2211.14475/) (2022)

## Abstract (arXiv)

Text logo design heavily relies on the creativity and expertise of professional designers, in which arranging element layouts is one of the most important procedures. However, few attention has been paid to this task which needs to take many factors (e.g., fonts, linguistics, topics, etc.) into consideration. In this paper, we propose a content-aware layout generation network which takes glyph images and their corresponding text as input and synthesizes aesthetic layouts for them automatically. Specifically, we develop a dual-discriminator module, including a sequence discriminator and an image discriminator, to evaluate both the character placing trajectories and rendered shapes of synthesized text logos, respectively. Furthermore, we fuse the information of linguistics from texts and visual semantics from glyphs to guide layout prediction, which both play important roles in professional layout design. To train and evaluate our approach, we construct a dataset named as TextLogo3K, consisting of about 3,500 text logo images and their pixel-level annotations. Experimental studies on this dataset demonstrate the effectiveness of our approach for synthesizing visually-pleasing text logos and verify its superiority against the state of the art.