Large Multi-modality Models (LMMs) have made significant progress in visual understanding and generation, but they still face challenges in General Visual Editing, particularly in following complex instructions, preserving appearance consistency, and supporting flexible input formats. To address this gap, we introduce RISEBench, the first benchmark for evaluating Reasoning-Informed viSual Editing (RISE). RISEBench focuses on four key reasoning types: Temporal, Causal, Spatial, and Logical Reasoning. We curate high-quality test cases for each category and propose an evaluation framework that assesses Instruction Reasoning, Appearance Consistency, and Visual Plausibility with both human judges and an LMM-as-a-judge approach. Our experiments reveal that while GPT-4o-Native significantly outperforms other open-source and proprietary models, even this state-of-the-art system struggles with logical reasoning tasks, highlighting an area that remains underexplored. As an initial effort, RISEBench aims to provide foundational insights into reasoning-aware visual editing and to catalyze future research. Though still in its early stages, we are committed to continuously expanding and refining the benchmark to support more comprehensive, reliable, and scalable evaluations of next-generation multimodal systems. Our code and data will be released atthis https URL.
View on arXiv@article{zhao2025_2504.02826,
title={ Envisioning Beyond the Pixels: Benchmarking Reasoning-Informed Visual Editing },
author={ Xiangyu Zhao and Peiyuan Zhang and Kexian Tang and Hao Li and Zicheng Zhang and Guangtao Zhai and Junchi Yan and Hua Yang and Xue Yang and Haodong Duan },
journal={arXiv preprint arXiv:2504.02826},
year={ 2025 }
}