mlsanityData to 7 Oct 2026

LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators

arXiv 1901.06767 ↗First posted 21 Jan 2019cs.CVcited by 130

Not on the map. Why?

Cited by 20 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Layout is important for graphic design and scene generation. We propose a novel Generative Adversarial Network, called LayoutGAN, that synthesizes layouts by modeling geometric relations of different types of 2D elements. The generator of LayoutGAN takes as input a set of randomly-placed 2D graphic elements and uses self-attention modules to refine their labels and geometric parameters jointly to produce a realistic layout. Accurate alignment is critical for good layouts. We thus propose a novel differentiable wireframe rendering layer that maps the generated layout to a wireframe image, upon which a CNN-based discriminator is used to optimize the layouts in image space. We validate the effectiveness of LayoutGAN in various experiments including MNIST digit generation, document layout generation, clipart abstract scene generation and tangram graphic design.

Key facts. Cited by 130 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jan 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 22; 2022: 11; 2023: 31; 2024: 23; 2025: 25; 2026: 18.

130 papers.

Sort:

By area:

  1. 2023LayoutGPT: Compositional Visual Planning and Generation with Large Language Modelscited by 337
  2. 2023TextDiffuser: Diffusion Models as Text Painterscited by 223
  3. 2023LayoutDM: Discrete Diffusion Model for Controllable Layout Generationcited by 141
  4. 2023TextDiffuser-2: Unleashing the Power of Language Models for Text Renderingcited by 136
  5. 2021Screen2Words: Automatic Mobile UI Summarization with Multimodal Learningcited by 119
  6. 2021CanvasVAE: Learning to Generate Vector Graphic Documentscited by 93
  7. 2021Variational Transformer Networks for Layout Generationcited by 88
  8. 2021Learning to Compose Visual Relationscited by 72
  9. 2021BLT: Bidirectional Layout Transformer for Controllable Layout Generationcited by 69
  10. 2021SketchGen: Generating Constrained CAD Sketchescited by 68
  11. 2022DisCoScene: Spatially Disentangled Generative Radiance Fields for Controllable 3D-aware Scene Synthesiscited by 61
  12. 2022Composition-aware Graphic Layout GAN for Visual-textual Presentation Designscited by 59
38 more
  1. 2023Ctrl-Room: Controllable Text-to-3D Room Meshes Generation with Layout Constraintscited by 59
  2. 2021Attention, please! A survey of Neural Attention Models in Deep Learningcited by 50
  3. 2021Engineering Sketch Generation for Computer-Aided Designcited by 49
  4. 2022LayoutFormer++: Conditional Graphic Layout Generation via Constraint Serialization and Decoding Space Restrictioncited by 43
  5. 2023Unifying Layout Generation with a Decoupled Diffusion Modelcited by 41
  6. 2023LayoutDiffusion: Improving Graphic Layout Generation by Discrete Diffusion Probabilistic Modelscited by 40
  7. 2024PosterLLaVa: Constructing a Unified Multi-modal Layout Generator with LLMcited by 39
  8. 2024Graphic Design with Large Multimodal Modelcited by 38
  9. 2021VINS: Visual Search for Mobile User Interface Designcited by 37
  10. 2023Relation-Aware Diffusion Model for Controllable Poster Layout Generationcited by 36
  11. 2024OpenCOLE: Towards Reproducible Automatic Graphic Design Generationcited by 36
  12. 2023Retrieval-Augmented Layout Transformer for Content-Aware Layout Generationcited by 34
  13. 2024PosterLlama: Bridging Design Ability of Langauge Model to Contents-Aware Layout Generationcited by 34
  14. 2022Geometry Aligned Variational Transformer for Image-conditioned Layout Generationcited by 33
  15. 2023Towards Flexible Multi-modal Document Modelscited by 30
  16. 2024UICoder: Finetuning Large Language Models to Generate User Interface Code through Automated Feedbackcited by 26
  17. 2023PLay: Parametrically Conditioned Layout Generation using Latent Diffusioncited by 24
  18. 2023Dolfin: Diffusion Layout Transformers without Autoencodercited by 23
  19. 2023CityGen: Infinite and Controllable City Layout Generationcited by 22
  20. 2023Diffusion-based Document Layout Generationcited by 21
  21. 2024LayoutFlow: Flow Matching for Layout Generationcited by 21
  22. 2023DLT: Conditioned layout generation with Joint Discrete-Continuous Diffusion Layout Transformercited by 20
  23. 2022Aesthetic Text Logo Synthesis via Content-aware Layout Inferringcited by 19
  24. 2024Desigen: A Pipeline for Controllable Design Template Generationcited by 19
  25. 2021DocSynth: A Layout Guided Approach for Controllable Document Image Synthesiscited by 17
  26. 2023LayoutNUWA: Revealing the Hidden Layout Expertise of Large Language Modelscited by 17
  27. 2025Generative AI for Cel-Animation: A Surveycited by 17
  28. 2025CreatiPoster: Towards Editable and Controllable Multi-Layer Graphic Design Generationcited by 17
  29. 2022LayoutDETR: Detection Transformer Is a Good Multimodal Layout Designercited by 15
  30. 2023GlobalMapper: Arbitrary-Shaped Urban Layout Generationcited by 15
  31. 2023A Parse-Then-Place Approach for Generating Graphic Layouts from Textual Descriptionscited by 15
  32. 2023UI Layout Generation with LLMs Guided by UI Grammarcited by 15
  33. 2023Planning and Rendering: Towards Product Poster Generation with Diffusion Modelscited by 15
  34. 2024SciPostLayout: A Dataset for Layout Analysis and Layout Generation of Scientific Posterscited by 14
  35. 2024VASCAR: Content-Aware Layout Generation via Visual-Aware Self-Correctioncited by 14
  36. 2021House-GAN++: Generative Adversarial Layout Refinement Networkscited by 13
  37. 2023Unsupervised Domain Adaption with Pixel-level Discriminator for Image-aware Layout Generationcited by 13
  38. 2021Automated Urban Planning for Reimagining City Configuration via Adversarial Learning: Quantification, Generation, and Evaluationcited by 11

50 most cited of 130 All 130 (JSON)