UniREditBench: A Unified Reasoning-based Image Editing Benchmark

Feng Han1,2*, Yibin Wang1,2*, Chenglin Li2,3, Zheming Liang2, Dianyi Wang1,2,
Yang Jiao1, Zhipeng Wei4, Chao Gong1, Cheng Jin1,2, Jingjing Chen1†, Jiaqi Wang2†
1Fudan University, 2Shanghai Innovation Intuition, 3Zhejiang University 4UC Berkeley
Paper 😊 UniREditBench Code
😊 UniREdit-Data-100K 😊 UniREdit-Bagel 😊 Leaderboard 😊 Gallery

UniREditBench Overview

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Benchmark Comparison

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Reasoning-based image editing benchmark comparison.
UniREditBench excels in broader scenario and evaluation dimension coverage.

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Image editing evaluation comparison.
Current text-reference-only evaluation potentially leads to misjudging, while our dual-reference evaluation results in more reliable assessments.

Evaluation Dimensions

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Qualitative Cases.
We present qualitative examples for each dimension across both real-world and game-world scenarios.

Multi-scenario Data Synthesis Pipeline

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Benchmarking Results on UniREditBench

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UniREDit-Data-100K

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UniREdit-Bagel

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Out-of-distribution Results Comparison on RISEBench

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Out-of-distribution Results Comparison on KRISBench

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BibTeX

@article{unireditbench,
  title={UniREditBench: A Unified Reasoning-based Image Editing Benchmark},
  author={Han, Feng and Wang, Yibin and Li, Chenglin and Liang, Zheming and Wang, Dianyi and Jiao, Yang and Wei, Zhipeng and Gong, Chao and Jin, Cheng and Chen, Jingjing and others},
  journal={arXiv preprint arXiv:2511.01295},
  year={2025}
}