LTSim: Layout Transportation-based Similarity Measure for Evaluating Layout Generation
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Abstract (arXiv)
We introduce a layout similarity measure designed to evaluate the results of layout generation. While several similarity measures have been proposed in prior research, there has been a lack of comprehensive discussion about their behaviors. Our research uncovers that the majority of these measures are unable to handle various layout differences, primarily due to their dependencies on strict element matching, that is one-by-one matching of elements within the same category. To overcome this limitation, we propose a new similarity measure based on optimal transport, which facilitates a more flexible matching of elements. This approach allows us to quantify the similarity between any two layouts even those sharing no element categories, making our measure highly applicable to a wide range of layout generation tasks. For tasks such as unconditional layout generation, where FID is commonly used, we also extend our measure to deal with collection-level similarities between groups of layouts. The empirical result suggests that our collection-level measure offers more reliable comparisons than existing ones like FID and Max.IoU.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 28 of its 41 references that are in the map. First posted on arXiv 17 Jul 2024.
Builds on
28 of 41 references.
References · 28
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- 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,512
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- 2022MaskGIT: Masked Generative Image Transformercited by 1,099
- 2021Vector Quantized Diffusion Model for Text-to-Image Synthesiscited by 774
- 2019PubLayNet: largest dataset ever for document layout analysiscited by 351
- 2022CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformerscited by 307
- 2023LayoutDM: Discrete Diffusion Model for Controllable Layout Generationcited by 143
- 2020LayoutTransformer: Layout Generation and Completion with Self-attentioncited by 123
- 2019LayoutVAE: Stochastic Scene Layout Generation From a Label Setcited by 119
- 2021CanvasVAE: Learning to Generate Vector Graphic Documentscited by 95
- 2021Variational Transformer Networks for Layout Generationcited by 88
16 more
- 2019Neural Design Network: Graphic Layout Generation with Constraintscited by 83
- 2021Constrained Graphic Layout Generation via Latent Optimizationcited by 83
- 2021BLT: Bidirectional Layout Transformer for Controllable Layout Generationcited by 69
- 2023LayoutDM: Transformer-based Diffusion Model for Layout Generationcited by 57
- 2019READ: Recursive Autoencoders for Document Layout Generationcited by 51
- 2023LayoutPrompter: Awaken the Design Ability of Large Language Modelscited by 48
- 2022LayoutFormer++: Conditional Graphic Layout Generation via Constraint Serialization and Decoding Space Restrictioncited by 43
- 2023Unifying Layout Generation with a Decoupled Diffusion Modelcited by 41
- 2023LayoutDiffusion: Improving Graphic Layout Generation by Discrete Diffusion Probabilistic Modelscited by 41
- 2023PLay: Parametrically Conditioned Layout Generation using Latent Diffusioncited by 24
- 2023Diffusion-based Document Layout Generationcited by 21
- 2023DLT: Conditioned layout generation with Joint Discrete-Continuous Diffusion Layout Transformercited by 20
- 2020LayoutGMN: Neural Graph Matching for Structural Layout Similaritycited by 19
- 2022Optimal Correction Cost for Object Detection Evaluationcited by 11
- 2023A Survey for Graphic Design Intelligencecited by 9
- 2023Diverse Multimedia Layout Generation with Multi Choice Learningcited by 8
Built on by
Citing papers per year: 2024: 2; 2025: 5; 2026: 3.
10 papers.
- 2025LLM/Agent-as-Data-Analyst: A Surveycited by 16
- 2024VASCAR: Content-Aware Layout Generation via Visual-Aware Self-Correctioncited by 15
- 2025LayoutCoT: Unleashing the Deep Reasoning Potential of Large Language Models for Layout Generationcited by 7
- 2025SciPostGen: Bridging the Gap between Scientific Papers and Poster Layoutscited by 4
- 2026Beyond Human Annotation: Recent Advances in Data Generation Methods for Document Intelligencecited by 4
- 2025LayoutRAG: Retrieval-Augmented Model for Content-agnostic Conditional Layout Generationcited by 3
- 2025SpA2V: Harnessing Spatial Auditory Cues for Audio-driven Spatially-aware Video Generationcited by 2
- 2024AutoGameUI: Constructing High-Fidelity GameUI via Multimodal Correspondence Matchingcited by 1
- 2026Brief2Design: A Multi-phased, Compositional Approach to Prompt-based Graphic Designcited by 0
- 2026Through the PRISM: Principle-Aware, Interpretable, and Multi-Scale Evaluation of Visual Designscited by 0
- 2026Through the PRISM: Principle-Aware, Interpretable, and Multi-Scale Evaluation of Visual Designs30 May 2026
- 2026Brief2Design: A Multi-phased, Compositional Approach to Prompt-based Graphic Design13 Apr 2026
- 2026Beyond Human Annotation: Recent Advances in Data Generation Methods for Document Intelligence18 Jan 2026
- 2025SciPostGen: Bridging the Gap between Scientific Papers and Poster Layouts27 Nov 2025
- 2025LLM/Agent-as-Data-Analyst: A Survey28 Sep 2025
- 2025SpA2V: Harnessing Spatial Auditory Cues for Audio-driven Spatially-aware Video Generation1 Aug 2025
- 2025LayoutRAG: Retrieval-Augmented Model for Content-agnostic Conditional Layout Generation3 Jun 2025
- 2025LayoutCoT: Unleashing the Deep Reasoning Potential of Large Language Models for Layout Generation15 Apr 2025
- 2024VASCAR: Content-Aware Layout Generation via Visual-Aware Self-Correction5 Dec 2024
- 2024AutoGameUI: Constructing High-Fidelity GameUI via Multimodal Correspondence Matching6 Nov 2024