mlsanityData to 7 Oct 2026

Automatic Text Box Placement for Supporting Typographic Design

arXiv 2510.07665 ↗First posted 9 Oct 2025cs.CVcited by 0

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Abstract (arXiv)

In layout design for advertisements and web pages, balancing visual appeal and communication efficiency is crucial. This study examines automated text box placement in incomplete layouts, comparing a standard Transformer-based method, a small Vision and Language Model (Phi3.5-vision), a large pretrained VLM (Gemini), and an extended Transformer that processes multiple images. Evaluations on the Crello dataset show the standard Transformer-based models generally outperform VLM-based approaches, particularly when incorporating richer appearance information. However, all methods face challenges with very small text or densely populated layouts. These findings highlight the benefits of task-specific architectures and suggest avenues for further improvement in automated layout design.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 19 references that are in the map. First posted on arXiv 9 Oct 2025.

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15 of 19 references.

References · 15

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2024Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phonecited by 1,883
  4. 2019Distance-IoU Loss: Faster and Better Learning for Bounding Box Regressioncited by 454
  5. 2023LayoutGPT: Compositional Visual Planning and Generation with Large Language Modelscited by 337
  6. 2023LayoutDM: Discrete Diffusion Model for Controllable Layout Generationcited by 141
  7. 2019LayoutGAN: Generating Graphic Layouts with Wireframe Discriminatorscited by 130
  8. 2019LayoutVAE: Stochastic Scene Layout Generation From a Label Setcited by 119
  9. 2021CanvasVAE: Learning to Generate Vector Graphic Documentscited by 93
  10. 2023LayoutPrompter: Awaken the Design Ability of Large Language Modelscited by 47
  11. 2024PosterLLaVa: Constructing a Unified Multi-modal Layout Generator with LLMcited by 39
  12. 2023Relation-Aware Diffusion Model for Controllable Poster Layout Generationcited by 36
3 more
  1. 2023Retrieval-Augmented Layout Transformer for Content-Aware Layout Generationcited by 34
  2. 2024PosterLlama: Bridging Design Ability of Langauge Model to Contents-Aware Layout Generationcited by 34
  3. 2023Towards Flexible Multi-modal Document Modelscited by 30

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  1. 2025Content-Aware Ad Banner Layout Generation with Two-Stage Chain-of-Thought in Vision Language Models
  2. 2026Seeing is Improving: Visual Feedback for Iterative Text Layout Refinement
  3. 2024GLDesigner: Leveraging Multi-Modal LLMs as Designer for Enhanced Aesthetic Text Glyph Layouts
  4. 2025AesthetiQ: Enhancing Graphic Layout Design via Aesthetic-Aware Preference Alignment of Multi-modal Large Language Models
  5. 2024LayoutDiT: Exploring Content-Graphic Balance in Layout Generation with Diffusion Transformer
  6. 2024Multimodal Markup Document Models for Graphic Design Completion
  7. 2025T-Stars-Poster: A Framework for Product-Centric Advertising Image Design
  8. 2024Towards Aligned Layout Generation via Diffusion Model with Aesthetic Constraints