T2I-ReasonBench: Benchmarking Reasoning-Informed Text-to-Image Generation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914002381045760 |
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| author | Sun, Kaiyue Fang, Rongyao Duan, Chengqi Liu, Xian Liu, Xihui |
| author_facet | Sun, Kaiyue Fang, Rongyao Duan, Chengqi Liu, Xian Liu, Xihui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17472 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | T2I-ReasonBench: Benchmarking Reasoning-Informed Text-to-Image Generation Sun, Kaiyue Fang, Rongyao Duan, Chengqi Liu, Xian Liu, Xihui Computer Vision and Pattern Recognition 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. |
| title | T2I-ReasonBench: Benchmarking Reasoning-Informed Text-to-Image Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.17472 |