SpotEdit: Evaluating Visually-Guided Image Editing Methods

Sara Ghazanfari, Wei-An Lin, Haitong Tian, Ersin Yumer

SpotEdit: Evaluating Visually-Guided Image Editing Methods: 3 upvotes on Hugging Face Daily Papers, #19 of 23 papers on 2025-08-26. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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