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Research map / Image generation / Autoregressive and latent generation

Layout generation

Generate graphic and design layouts with transformers and diffusion models.

39 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2019–2026) · an approach in Autoregressive and latent generation, keyword cluster “motion · layout · object”

State History Tried Checklist Preprints Who Export

Where it stands

Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.

18 papers in 2023–24 and 12 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic shrank, from 3.8% to 1.3%.

How this is decided

A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.

How it got here

2016: 0162017: 0172018: 0182019: 2192020: 1202021: 2212022: 4222023: 9232024: 9242025: 4252026: 826

Most cited paper of each year

Earliest papers here

What has been tried

31 papers here report 347 measured changes (a component added, removed or swapped, compared with a baseline): helped 73%, no clear effect 3%, hurt 22%, mixed 2%. Papers mostly publish changes that helped, so "helped" is over-represented.

Ablations in recent papers

What was changedMeasured onResultEffectPaper
add retrieved-layout feature fL and input-image feature fI alongside fCPKU test · FID4.67 -> 3.45HelpedRetrieval-Augmented Layout Transformer for Content-Aware Layout Generation (CVPR 2024 · 21 citations)
Use the saliency map mask instead of a full mask for the attention constraintWeb-design test set · FID34.49 -> 31.52HelpedDesigen: A Pipeline for Controllable Design Template Generation (CVPR 2024 · 14 citations)
Remove conditioning on previously predicted attributesRPLAN test, boundary-only input · fidimg4.182 -> 23.103HurtMaskPLAN: Masked Generative Layout Planning from Partial Input (CVPR 2024 · 14 citations)
Add the image diffusion domain to vector-only VLCCrello · composed image FID5.74 -> 5.83HurtVisual Layout Composer: Image-Vector Dual Diffusion Model for Design Layout Generation (CVPR 2024 · 12 citations)
Use Qwen1.5-7B instead of Qwen1.5-0.5BCrello · S_GI6.35 -> 6.90HelpedGraphic Design with Large Multimodal Model (AAAI 2025 · 10 citations)
Add UGC to the HiCo-base configuration with GBB and mask FuseNetHiCo-7K ablation · FID15.26 -> 16.85HurtHiCo: Hierarchical Controllable Diffusion Model for Layout-to-image Generation (NeurIPS 2024 · 5 citations)
use layered design composition with rendered previous layers as contextCrello-v4 design composition · LLaVA-OV design and layout7.84 -> 8.08HelpedFrom Elements to Design: A Layered Approach for Automatic Graphic Design Composition (CVPR 2025 · 3 citations)
Use pixel-space wireframe input instead of object-space sequence inputPubLaynet · F129.52 -> 59.82HelpedSpot the Error: Non-autoregressive Graphic Layout Generation with Wireframe Locator (AAAI 2024 · 3 citations)
Add amodal mask supervision to CreatiLayoutOverLayBench-Simple · O-mIoU (%)32.52 -> 37.69; +15.90%HelpedOverLayBench: A Benchmark for Layout-to-Image Generation with Dense Overlaps (NeurIPS 2025 · 1 citation)
Add aesthetic constraints to LACEPubLayNet unconditional · Alignment0.238 -> 0.141HelpedTowards Aligned Layout Generation via Diffusion Model with Aesthetic Constraints (ICLR 2024 · 1 citation)
Train the adversary jointly instead of keeping it fixedZelda game levels · % Unique0.22 -> 0.995HelpedGenCO: Generating Diverse Designs with Combinatorial Constraints (ICML 2024 · 0 citations)
add Plücker manifold parameterizationPartNet Mobility, unconditional generation · MMD0.0270 -> 0.0264HelpedMIDGArD: Modular Interpretable Diffusion over Graphs for Articulated Designs (NeurIPS 2024 · 0 citations)

What reviewers will expect

Datasets and baselines named in the 6 papers of this approach from 2025–2026 that have a full-text summary. Leaving out the common ones is a frequent review question.

Datasets

  • COCO · 2 of 6 papers
  • LayoutSAM · 2 of 6 papers

Baselines

  • FlexDM · 2 of 6 papers
  • GLIGEN · 2 of 6 papers
  • InstanceDiffusion · 2 of 6 papers
  • MIGC · 2 of 6 papers
  • HiCo · 2 of 6 papers
  • 3DIS · 2 of 6 papers

Recent preprints

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    Who publishes on it

    Institutions on the most papers of this approach since 2023 (of 30 papers; from OpenAlex and the papers' affiliations).

    Use it

    Download BibTeX of 36 key papersOpen the topic in the interactive map

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    Same keyword cluster

    Elsewhere in the topic

    Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).

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