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WorldGenBench: A World-Knowledge-Integrated Benchmark for Reasoning-Driven Text-to-Image Generation

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arxiv 2505.01490 v1 pith:755QN4AX submitted 2025-05-02 cs.CV

Daoan Zhang , Che Jiang , Ruoshi Xu , Biaoxiang Chen , Zijian Jin , Yutian Lu , Jianguo Zhang , Liang Yong
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Jiebo Luo Shengda Luo
This is my paper
classification cs.CV
keywords modelsknowledgeworldgenbenchbenchmarkcapabilitiesdwanzhang-aigenerationgithub
verification ladder T0 review T1 audit T2 compute T3 formal
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Recent advances in text-to-image (T2I) generation have achieved impressive results, yet existing models still struggle with prompts that require rich world knowledge and implicit reasoning: both of which are critical for producing semantically accurate, coherent, and contextually appropriate images in real-world scenarios. To address this gap, we introduce \textbf{WorldGenBench}, a benchmark designed to systematically evaluate T2I models' world knowledge grounding and implicit inferential capabilities, covering both the humanities and nature domains. We propose the \textbf{Knowledge Checklist Score}, a structured metric that measures how well generated images satisfy key semantic expectations. Experiments across 21 state-of-the-art models reveal that while diffusion models lead among open-source methods, proprietary auto-regressive models like GPT-4o exhibit significantly stronger reasoning and knowledge integration. Our findings highlight the need for deeper understanding and inference capabilities in next-generation T2I systems. Project Page: \href{https://dwanzhang-ai.github.io/WorldGenBench/}{https://dwanzhang-ai.github.io/WorldGenBench/}

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ToolArtist: Tool-Using Unified Multimodal Models for Agentic Image Generation

    cs.CV 2026-08 conditional novelty 6.0 of 10

    ToolArtist trains a unified multimodal model to reason, search the web, and generate images as one policy, improving scores on WISE and WorldGenBench-Humanities.

  2. OmniPhys: Knowledge-Graph-Driven Benchmarking and Collective Optimization for Physical Commonsense in Text-to-Image Generation

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A knowledge-graph benchmark (OmniPhys, 1,551 prompts, 14 physics knowledge points) and a batch-feedback prompt optimizer (OmniPrompt) improve measured physical consistency of text-to-image models by 0.01–0.03 Joint Sc...

  3. Boogu-Image-0.1: Boosting Open Agentic Multimodal Generation via Understanding under a Minimal Budget

    cs.CV 2026-07 conditional novelty 4.0 of 10

    An open-source image-generation family shows that agentic prompt rewriting and a stronger text encoder can lift quality to near closed-source levels with only 208.62M images and about $400K of training compute.

  4. T2VWorldBench: A Benchmark for Evaluating World Knowledge in Text-to-Video Generation

    cs.CV 2025-07 reject novelty 4.0 of 10

    A 1,200-prompt benchmark across six world-knowledge domains reports that ten state-of-the-art text-to-video models average below 0.70 on a 0 to 1 scale for producing videos consistent with real-world knowledge.

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