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arxiv: 2508.17472 · v1 · pith:QVKL673M · submitted 2025-08-24 · cs.CV

T2I-ReasonBench: Benchmarking Reasoning-Informed Text-to-Image Generation

Kaiyue Sun , Rongyao Fang , Chengqi Duan , Xian Liu , Xihui Liu This is my paper

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classification cs.CV
keywords benchmarkgenerationimagemodelsproposereasoningt2i-reasonbenchtext-to-image
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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.

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