Envisioning Beyond the Pixels: Benchmarking Reasoning-Informed Visual
  Editing
Authors: name: Xiangyu Zhao, name: Peiyuan Zhang, name: Kexian Tang, name: Hao Li, name: Zicheng Zhang, name: Guangtao Zhai, name: Junchi Yan, name: Hua Yang, name: Xue Yang, name: Haodong Duan

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 at
https://github.com/PhoenixZ810/RISEBench.