UniREditBench: A Unified Reasoning-based Image Editing Benchmark

TL;DR

UniREditBench is a comprehensive, multimodal benchmark designed to evaluate reasoning-based image editing capabilities across diverse scenarios, addressing limitations of existing benchmarks with new evaluation methods and a large synthetic dataset.

Contribution

The paper introduces UniREditBench, a unified benchmark with multimodal evaluation and a large synthetic dataset, enabling systematic assessment of reasoning in image editing models.

Findings

Enhanced evaluation reliability with multimodal dual-reference approach.

Significant performance improvements of fine-tuned models on diverse scenarios.

Benchmarking reveals strengths and weaknesses of current image editing models.

Abstract

Recent advances in multi-modal generative models have driven substantial improvements in image editing. However, current generative models still struggle with handling diverse and complex image editing tasks that require implicit reasoning, underscoring the need for a comprehensive benchmark to systematically assess their performance across various reasoning scenarios. Existing benchmarks primarily focus on single-object attribute transformation in realistic scenarios, which, while effective, encounter two key challenges: (1) they largely overlook multi-object interactions as well as game-world scenarios that involve human-defined rules, which are common in real-life applications; (2) they only rely on textual references to evaluate the generated images, potentially leading to systematic misjudgments, especially in complex reasoning scenarios. To this end, this work proposes UniREditBench, a unified benchmark for reasoning-based image editing evaluation. It comprises 2,700 meticulously curated samples, covering both real- and game-world scenarios across 8 primary dimensions and 18 sub-dimensions. To improve evaluation reliability, we introduce multimodal dual-reference evaluation, providing both textual and ground-truth image references for each sample assessment. Furthermore, we design an automated multi-scenario data synthesis pipeline and construct UniREdit-Data-100K, a large-scale synthetic dataset with high-quality chain-of-thought (CoT) reasoning annotations. We fine-tune Bagel on this dataset and develop UniREdit-Bagel, demonstrating substantial improvements in both in-domain and out-of-distribution settings. Through thorough benchmarking of both open-source and closed-source image editing models, we reveal their strengths and weaknesses across various aspects.