title: Envisioning Beyond the Pixels: Benchmarking Reasoning-Informed Visual
    Editing
summary: 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.
paper_authors._id: 67ef4be0985aa66b67021ddc
paper_authors.name: Xiangyu Zhao
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paper_authors.name: Peiyuan Zhang
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paper_authors.name: Kexian Tang
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paper_authors.fullname: Kexian Tang
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paper_authors.user: KexianTang
paper_authors._id: 67ef4be0985aa66b67021ddf
paper_authors.name: Hao Li
paper_authors._id: 67ef4be0985aa66b67021de0
paper_authors.name: Zicheng Zhang
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paper_authors.fullname: Zicheng Zhang
paper_authors.type: user
paper_authors.user: zhangzicheng
paper_authors._id: 67ef4be0985aa66b67021de1
paper_authors.name: Guangtao Zhai
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paper_authors.fullname: Guangtao Zhai
paper_authors.type: user
paper_authors.user: GTZhai
paper_authors._id: 67ef4be0985aa66b67021de2
paper_authors.name: Junchi Yan
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/916befcbf0e52ce56be49617f31c7bb2.svg
paper_authors.fullname: Junchi Yan
paper_authors.type: user
paper_authors.user: Rethinker
paper_authors._id: 67ef4be0985aa66b67021de3
paper_authors.name: Hua Yang
paper_authors._id: 67ef4be0985aa66b67021de4
paper_authors.name: Xue Yang
paper_authors.status: claimed_verified
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paper_authors.fullname: Xue Yang
paper_authors.type: user
paper_authors.user: yangxue
paper_authors._id: 67ef4be0985aa66b67021de5
paper_authors.name: Haodong Duan
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paper_authors.fullname: HAODONG DUAN
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paper_authors.user: KennyUTC
paper_title: Envisioning Beyond the Pixels: Benchmarking Reasoning-Informed Visual
    Editing
paper_summary: 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.
paper_ai_summary: RISEBench evaluates Reasoning-Informed Visual Editing by assessing temporal, causal, spatial, and logical reasoning in multimodal models like GPT-4o-Native.
paper_ai_keywords: Reasoning-Informed viSual Editing Temporal Causal Spatial Logical Reasoning Instruction Reasoning Appearance Consistency Visual Plausibility LMM-as-a-judge multimodal models GPT-4o-Native