PosterIQ: A Design Perspective Benchmark for Poster Understanding and Generation
- URL: http://arxiv.org/abs/2603.24078v1
- Date: Wed, 25 Mar 2026 08:33:51 GMT
- Title: PosterIQ: A Design Perspective Benchmark for Poster Understanding and Generation
- Abstract summary: PosterIQ is a design-driven benchmark for poster understanding and generation.<n>It includes 7,765 image-annotation instances and 822 generation prompts spanning real, professional, and synthetic cases.
- Score: 27.097615059097322
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present PosterIQ, a design-driven benchmark for poster understanding and generation, annotated across composition structure, typographic hierarchy, and semantic intent. It includes 7,765 image-annotation instances and 822 generation prompts spanning real, professional, and synthetic cases. To bridge visual design cognition and generative modeling, we define tasks for layout parsing, text-image correspondence, typography/readability and font perception, design quality assessment, and controllable, composition-aware generation with metaphor. We evaluate state-of-the-art MLLMs and diffusion-based generators, finding persistent gaps in visual hierarchy, typographic semantics, saliency control, and intention communication; commercial models lead on high-level reasoning but act as insensitive automatic raters, while generators render text well yet struggle with composition-aware synthesis. Extensive analyses show PosterIQ is both a quantitative benchmark and a diagnostic tool for design reasoning, offering reproducible, task-specific metrics. We aim to catalyze models' creativity and integrate human-centred design principles into generative vision-language systems.
Related papers
- Text-Aided Multi-Modal Panoptic Symbol Spotting for CAD Floor Plan Drawings [11.891289170899425]
TextCAD is a framework that models graphical primitives and textual annotations for panoptic symbol spotting.<n>We show that TextCAD effectively improves symbol spotting performance and state-of-the-art results.
arXiv Detail & Related papers (2026-07-14T12:07:44Z) - Graphic-Design-Bench: A Comprehensive Benchmark for Evaluating AI on Graphic Design Tasks [7.841779848822317]
GraphicDesignBench (GDB) is the first comprehensive benchmark suite designed specifically to evaluate AI models on the full breadth of professional graphic design tasks.<n>The suite comprises 50 tasks organized along five axes: layout, typography, infographics, template & design semantics and animation.<n>We evaluate a set of frontier closed-source models using a standardized metric taxonomy covering spatial accuracy, perceptual quality, text fidelity, semantic alignment, and structural validity.
arXiv Detail & Related papers (2026-04-05T17:20:08Z) - LogiStory: A Logic-Aware Framework for Multi-Image Story Visualization [59.35938978648807]
We propose a logic-aware multi-image story visualization framework, LogiStory.<n>The framework is built around the central innovation of explicitly modeling visual logic in story visualization.<n>This work provides a foundational step towards modeling and enforcing visual logic in general image sequence and video generation tasks.
arXiv Detail & Related papers (2026-03-30T06:37:12Z) - Beyond Pixels: Visual Metaphor Transfer via Schema-Driven Agentic Reasoning [56.24016465596292]
A visual metaphor constitutes a high-order form of human creativity, employing cross-domain semantic fusion to transform abstract concepts into impactful visual rhetoric.<n>We introduce the task of Visual Metaphor Transfer (VMT), which challenges models to autonomously decouple the "creative essence" from a reference image and re-materialize that abstract logic onto a user-specified subject.<n>Our method significantly outperforms SOTA baselines in metaphor consistency, analogy appropriateness, and visual creativity, paving the way for automated high-impact creative applications in advertising and media.
arXiv Detail & Related papers (2026-02-01T17:01:36Z) - CAL-RAG: Retrieval-Augmented Multi-Agent Generation for Content-Aware Layout Design [6.830055289299306]
CAL-RAG is a retrieval-augmented, agentic framework for content-aware layout generation.<n>We implement our framework using LangGraph and evaluate it on a benchmark rich in semantic variability.<n>Results demonstrate that combining retrieval augmentation with agentic multi-step reasoning yields a scalable, interpretable, and high-fidelity solution.
arXiv Detail & Related papers (2025-06-27T06:09:56Z) - KITTEN: A Knowledge-Intensive Evaluation of Image Generation on Visual Entities [93.74881034001312]
KITTEN is a benchmark for Knowledge-InTensive image generaTion on real-world ENtities.<n>We conduct a systematic study of the latest text-to-image models and retrieval-augmented models.<n>Analysis shows that even advanced text-to-image models fail to generate accurate visual details of entities.
arXiv Detail & Related papers (2024-10-15T17:50:37Z) - MetaDesigner: Advancing Artistic Typography Through AI-Driven, User-Centric, and Multilingual WordArt Synthesis [65.78359025027457]
MetaDesigner introduces a transformative framework for artistic typography, powered by Large Language Models (LLMs)<n>Its foundation is a multi-agent system comprising the Pipeline, Glyph, and Texture agents, which collectively orchestrate the creation of customizable WordArt.
arXiv Detail & Related papers (2024-06-28T11:58:26Z) - Evaluation Metrics for Automated Typographic Poster Generation [0.24578723416255752]
We propose a set of metrics for typographic design evaluation, focusing on their legibility.
We also integrate emotion recognition to identify text semantics automatically and analyse the performance of the approach.
arXiv Detail & Related papers (2024-02-10T13:18:10Z) - Improving Generation and Evaluation of Visual Stories via Semantic
Consistency [72.00815192668193]
Given a series of natural language captions, an agent must generate a sequence of images that correspond to the captions.
Prior work has introduced recurrent generative models which outperform synthesis text-to-image models on this task.
We present a number of improvements to prior modeling approaches, including the addition of a dual learning framework.
arXiv Detail & Related papers (2021-05-20T20:42:42Z) - Matching Visual Features to Hierarchical Semantic Topics for Image
Paragraph Captioning [50.08729005865331]
This paper develops a plug-and-play hierarchical-topic-guided image paragraph generation framework.
To capture the correlations between the image and text at multiple levels of abstraction, we design a variational inference network.
To guide the paragraph generation, the learned hierarchical topics and visual features are integrated into the language model.
arXiv Detail & Related papers (2021-05-10T06:55:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.