T2I-ReasonBench: Benchmarking Reasoning-Informed Text-to-Image
  Generation
We propose T2I-ReasonBench, a benchmark evaluating reasoning capabilities of
text-to-image (T2I) models. It consists of four dimensions: Idiom
Interpretation, Textual Image Design, Entity-Reasoning and
Scientific-Reasoning. We propose a two-stage evaluation protocol to assess the
reasoning accuracy and image quality. We benchmark various T2I generation
models, and provide comprehensive analysis on their performances.
68ad304f86b21a0e2e358e22
Kaiyue Sun
claimed_verified
2025-08-26T09:49:28.939Z
63640ce5ff4b318d1b7b6f5c
/avatars/9336d1dab1491f3c8e84b1ea287eb891.svg
Kaiyue Sun
user
Kaiyue
68ad304f86b21a0e2e358e23
Rongyao Fang
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Chengqi Duan
claimed_verified
2025-09-17T13:09:38.851Z
64a2b496e2e19de17db7de65
/avatars/241448ca487833d6cc5d57bb1fdb6ee5.svg
Duan Chengqi
user
gogoduan
68ad304f86b21a0e2e358e25
Xian Liu
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Xihui Liu
T2I-ReasonBench: Benchmarking Reasoning-Informed Text-to-Image
  Generation
We propose T2I-ReasonBench, a benchmark evaluating reasoning capabilities of
text-to-image (T2I) models. It consists of four dimensions: Idiom
Interpretation, Textual Image Design, Entity-Reasoning and
Scientific-Reasoning. We propose a two-stage evaluation protocol to assess the
reasoning accuracy and image quality. We benchmark various T2I generation
models, and provide comprehensive analysis on their performances.
T2I-ReasonBench evaluates the reasoning capabilities of text-to-image models across four dimensions using a two-stage protocol, analyzing their performance comprehensively.
text-to-image T2I Idiom Interpretation Textual Image Design Entity-Reasoning Scientific-Reasoning two-stage evaluation protocol