mlsanityData to 7 Oct 2026

Design-o-meter: Towards Evaluating and Refining Graphic Designs

arXiv 2411.14959 ↗First posted 22 Nov 2024cs.CV cs.AI cs.HCcited by 5

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Abstract (arXiv)

Graphic designs are an effective medium for visual communication. They range from greeting cards to corporate flyers and beyond. Off-late, machine learning techniques are able to generate such designs, which accelerates the rate of content production. An automated way of evaluating their quality becomes critical. Towards this end, we introduce Design-o-meter, a data-driven methodology to quantify the goodness of graphic designs. Further, our approach can suggest modifications to these designs to improve its visual appeal. To the best of our knowledge, Design-o-meter is the first approach that scores and refines designs in a unified framework despite the inherent subjectivity and ambiguity of the setting. Our exhaustive quantitative and qualitative analysis of our approach against baselines adapted for the task (including recent Multimodal LLM-based approaches) brings out the efficacy of our methodology. We hope our work will usher more interest in this important and pragmatic problem setting.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 23 of its 57 references that are in the map. First posted on arXiv 22 Nov 2024.

Builds on

23 of 57 references.

References · 23

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11 more
  1. 2020Attribute-conditioned Layout GAN for Automatic Graphic Designcited by 67
  2. 2023PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layoutcited by 60
  3. 2023FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive Learningcited by 48
  4. 2023COLE: A Hierarchical Generation Framework for Multi-Layered and Editable Graphic Designcited by 39
  5. 2024Graphic Design with Large Multimodal Modelcited by 38
  6. 2023Retrieval-Augmented Layout Transformer for Content-Aware Layout Generationcited by 34
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026TASTE: A Designer-Annotated Multi-Dimensional Preference Dataset for AI-Generated Graphic Design
  2. 2026Can Vision Language Models Assess Graphic Design Aesthetics? A Benchmark, Evaluation, and Dataset Perspective
  3. 2026Graphic-Design-Bench: A Comprehensive Benchmark for Evaluating AI on Graphic Design Tasks
  4. 2024DesignProbe: A Graphic Design Benchmark for Multimodal Large Language Models
  5. 2026DesignSense: A Human Preference Dataset and Reward Modeling Framework for Graphic Layout Generation
  6. 2026GDB-Reward: From Evaluation Metrics to Training Rewards for Graphic Design
  7. 2026PosterIQ: A Design Perspective Benchmark for Poster Understanding and Generation
  8. 2024Generative Visual Communication in the Era of Vision-Language Models