I2I-Bench: A Comprehensive Benchmark Suite for Image-to-Image Editing Models
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918231477846016 |
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| author | Wang, Juntong Wang, Jiarui Duan, Huiyu Kang, Jiaxiang Zhai, Guangtao Min, Xiongkuo |
| author_facet | Wang, Juntong Wang, Jiarui Duan, Huiyu Kang, Jiaxiang Zhai, Guangtao Min, Xiongkuo |
| contents | Image editing models are advancing rapidly, yet comprehensive evaluation remains a significant challenge. Existing image editing benchmarks generally suffer from limited task scopes, insufficient evaluation dimensions, and heavy reliance on manual annotations, which significantly constrain their scalability and practical applicability. To address this, we propose \textbf{I2I-Bench}, a comprehensive benchmark for image-to-image editing models, which features (i) diverse tasks, encompassing 10 task categories across both single-image and multi-image editing tasks, (ii) comprehensive evaluation dimensions, including 30 decoupled and fine-grained evaluation dimensions with automated hybrid evaluation methods that integrate specialized tools and large multimodal models (LMMs), and (iii) rigorous alignment validation, justifying the consistency between our benchmark evaluations and human preferences. Using I2I-Bench, we benchmark numerous mainstream image editing models, investigating the gaps and trade-offs between editing models across various dimensions. We will open-source all components of I2I-Bench to facilitate future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04660 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | I2I-Bench: A Comprehensive Benchmark Suite for Image-to-Image Editing Models Wang, Juntong Wang, Jiarui Duan, Huiyu Kang, Jiaxiang Zhai, Guangtao Min, Xiongkuo Computer Vision and Pattern Recognition Image editing models are advancing rapidly, yet comprehensive evaluation remains a significant challenge. Existing image editing benchmarks generally suffer from limited task scopes, insufficient evaluation dimensions, and heavy reliance on manual annotations, which significantly constrain their scalability and practical applicability. To address this, we propose \textbf{I2I-Bench}, a comprehensive benchmark for image-to-image editing models, which features (i) diverse tasks, encompassing 10 task categories across both single-image and multi-image editing tasks, (ii) comprehensive evaluation dimensions, including 30 decoupled and fine-grained evaluation dimensions with automated hybrid evaluation methods that integrate specialized tools and large multimodal models (LMMs), and (iii) rigorous alignment validation, justifying the consistency between our benchmark evaluations and human preferences. Using I2I-Bench, we benchmark numerous mainstream image editing models, investigating the gaps and trade-offs between editing models across various dimensions. We will open-source all components of I2I-Bench to facilitate future research. |
| title | I2I-Bench: A Comprehensive Benchmark Suite for Image-to-Image Editing Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.04660 |