WorldGenBench: A World-Knowledge-Integrated Benchmark for
  Reasoning-Driven Text-to-Image Generation
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
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
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:
https://dwanzhang-ai.github.io/WorldGenBench/{https://dwanzhang-ai.github.io/WorldGenBench/}
681965b8a52fff9bf3ff6441
Daoan Zhang
extracted_pending
2025-05-06T01:28:25.306Z
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/avatars/1f9f0db4eee887f9ed03dffc8c36ea23.svg
DaoanZhang
user
DwanZhang
681965b8a52fff9bf3ff6442
Che Jiang
681965b8a52fff9bf3ff6443
Ruoshi Xu
681965b8a52fff9bf3ff6444
Biaoxiang Chen
681965b8a52fff9bf3ff6445
Zijian Jin
681965b8a52fff9bf3ff6446
Yutian Lu
681965b8a52fff9bf3ff6447
Jianguo Zhang
681965b8a52fff9bf3ff6448
Liang Yong
681965b8a52fff9bf3ff6449
Jiebo Luo
681965b8a52fff9bf3ff644a
Shengda Luo
WorldGenBench: A World-Knowledge-Integrated Benchmark for
  Reasoning-Driven Text-to-Image Generation
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
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
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:
https://dwanzhang-ai.github.io/WorldGenBench/{https://dwanzhang-ai.github.io/WorldGenBench/}
WorldGenBench evaluates text-to-image models using the Knowledge Checklist Score to identify gaps in world knowledge grounding and implicit reasoning.
diffusion models auto-regressive models GPT-4o Knowledge Checklist Score