mlsanityData to 14 Oct 2026

LayoutVAE: Stochastic Scene Layout Generation From a Label Set

arXiv 1907.10719 ↗First posted 24 Jul 2019cs.CVcited by 119

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Cited by 24 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Recently there is an increasing interest in scene generation within the research community. However, models used for generating scene layouts from textual description largely ignore plausible visual variations within the structure dictated by the text. We propose LayoutVAE, a variational autoencoder based framework for generating stochastic scene layouts. LayoutVAE is a versatile modeling framework that allows for generating full image layouts given a label set, or per label layouts for an existing image given a new label. In addition, it is also capable of detecting unusual layouts, potentially providing a way to evaluate layout generation problem. Extensive experiments on MNIST-Layouts and challenging COCO 2017 Panoptic dataset verifies the effectiveness of our proposed framework.

Key facts. Cited by 119 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Jul 2019.

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Citing papers per year: 2021: 17; 2022: 9; 2023: 29; 2024: 31; 2025: 20; 2026: 13.

119 papers.

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  1. 2023LayoutGPT: Compositional Visual Planning and Generation with Large Language Modelscited by 347
  2. 2023TextDiffuser: Diffusion Models as Text Painterscited by 229
  3. 2021ATISS: Autoregressive Transformers for Indoor Scene Synthesiscited by 200
  4. 2023LayoutDM: Discrete Diffusion Model for Controllable Layout Generationcited by 143
  5. 2023TextDiffuser-2: Unleashing the Power of Language Models for Text Renderingcited by 140
  6. 2021CanvasVAE: Learning to Generate Vector Graphic Documentscited by 95
  7. 2021Variational Transformer Networks for Layout Generationcited by 88
  8. 2021SceneGen: Learning to Generate Realistic Traffic Scenescited by 84
  9. 2021Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes Using Scene Graphscited by 84
  10. 2021Constrained Graphic Layout Generation via Latent Optimizationcited by 83
  11. 2022Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesiscited by 75
  12. 2021BLT: Bidirectional Layout Transformer for Controllable Layout Generationcited by 69
38 more
  1. 2024EchoScene: Indoor Scene Generation via Information Echo over Scene Graph Diffusioncited by 67
  2. 2023Ctrl-Room: Controllable Text-to-3D Room Meshes Generation with Layout Constraintscited by 60
  3. 2022Composition-aware Graphic Layout GAN for Visual-textual Presentation Designscited by 59
  4. 2023LayoutDM: Transformer-based Diffusion Model for Layout Generationcited by 57
  5. 2023CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusioncited by 54
  6. 2023LayoutPrompter: Awaken the Design Ability of Large Language Modelscited by 48
  7. 2023Unifying Layout Generation with a Decoupled Diffusion Modelcited by 41
  8. 2023LayoutDiffusion: Improving Graphic Layout Generation by Discrete Diffusion Probabilistic Modelscited by 41
  9. 2023Relation-Aware Diffusion Model for Controllable Poster Layout Generationcited by 37
  10. 2024OpenCOLE: Towards Reproducible Automatic Graphic Design Generationcited by 37
  11. 2023Retrieval-Augmented Layout Transformer for Content-Aware Layout Generationcited by 35
  12. 2024PosterLlama: Bridging Design Ability of Langauge Model to Contents-Aware Layout Generationcited by 35
  13. 2022Geometry Aligned Variational Transformer for Image-conditioned Layout Generationcited by 33
  14. 2023Towards Flexible Multi-modal Document Modelscited by 31
  15. 2024Towards Aligned Layout Generation via Diffusion Model with Aesthetic Constraintscited by 26
  16. 2024CityCraft: A Real Crafter for 3D City Generationcited 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. 2023LayoutNUWA: Revealing the Hidden Layout Expertise of Large Language Modelscited by 19
  25. 2024Desigen: A Pipeline for Controllable Design Template Generationcited by 19
  26. 2025MMGDreamer: Mixed-Modality Graph for Geometry-Controllable 3D Indoor Scene Generationcited by 19
  27. 2021GUIGAN: Learning to Generate GUI Designs Using Generative Adversarial Networkscited by 17
  28. 2023Planning and Rendering: Towards Product Poster Generation with Diffusion Modelscited by 16
  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. 2024VASCAR: Content-Aware Layout Generation via Visual-Aware Self-Correctioncited by 15
  33. 2021House-GAN++: Generative Adversarial Layout Refinement Networkscited by 13
  34. 2023Unsupervised Domain Adaption with Pixel-level Discriminator for Image-aware Layout Generationcited by 13
  35. 2022CapOnImage: Context-driven Dense-Captioning on Imagecited by 11
  36. 2023Towards Diverse and Consistent Typography Generationcited by 11
  37. 2024UniGen: Unified Modeling of Initial Agent States and Trajectories for Generating Autonomous Driving Scenarioscited by 10
  38. 2024COHO: Context-Sensitive City-Scale Hierarchical Urban Layout Generationcited by 10

50 most cited of 119 All 119 (JSON)