SpotEdit: Evaluating Visually-Guided Image Editing Methods

• URL: http://arxiv.org/abs/2508.18159v2
• Date: Fri, 26 Sep 2025 19:05:06 GMT
• Title: SpotEdit: Evaluating Visually-Guided Image Editing Methods
• Authors: Sara Ghazanfari, Wei-An Lin, Haitong Tian, Ersin Yumer,
• Abstract summary: SpotEdit is a comprehensive benchmark designed to assess visually-guided image editing methods.<n>Our benchmark includes a dedicated component on hallucination, highlighting how leading models, such as GPT-4o, often hallucinate the existence of a visual cue and erroneously perform the editing task.
• Score: 3.5066378196008636
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visually-guided image editing, where edits are conditioned on both visual cues and textual prompts, has emerged as a powerful paradigm for fine-grained, controllable content generation. Although recent generative models have shown remarkable capabilities, existing evaluations remain simple and insufficiently representative of real-world editing challenges. We present SpotEdit, a comprehensive benchmark designed to systematically assess visually-guided image editing methods across diverse diffusion, autoregressive, and hybrid generative models, uncovering substantial performance disparities. To address a critical yet underexplored challenge, our benchmark includes a dedicated component on hallucination, highlighting how leading models, such as GPT-4o, often hallucinate the existence of a visual cue and erroneously perform the editing task. Our code and benchmark are publicly released at https://github.com/SaraGhazanfari/SpotEdit.

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