UniREditBench: A Unified Reasoning-based Image Editing Benchmark

Recent advances in multimodal generative models have drivensubstantial improvements in image editing. However, current generativemodels still struggle with handling diverse and complex image editingtasks that require implicit reasoning, underscoring the need for a compre-hensive benchmark to systematically assess their performance across var-ious reasoning scenarios. Existing benchmarks primarily focus on single-object attribute transformation in realistic scenarios, which, while effec-tive, encounter two key challenges: (1) they largely overlook multi-objectinteractions as well as game-world scenarios that involve human-definedrules, which are common in real-life applications; (2) they only rely ontextual references to evaluate the generated images, potentially leadingto systematic misjudgments, especially in complex reasoning scenarios.To this end, this work proposes UniREditBench, a unified benchmarkfor reasoning-based image editing evaluation. It comprises 2,700 metic-ulously curated samples, covering both real- and game-world scenariosacross 8 primary dimensions and 18 sub-dimensions. To improve evalua-tion reliability, we introduce multimodal dual-reference evaluation, pro-viding both textual and ground-truth image references for each sampleassessment. Furthermore, we design an automated multi-scenario datasynthesis pipeline and construct UniREdit-Data-100K, a large-scalesynthetic dataset with high-quality chain-of-thought (CoT) reasoning an-notations. We fine-tune Bagel on this dataset and develop UniREdit-Bagel, demonstrating substantial improvements in both in-domain andout-of-distribution settings. Through thorough benchmarking of bothopen-source and closed-source image editing models, we reveal theirstrengths and weaknesses across various aspects.