SpotEdit: Evaluating Visually-Guided Image Editing Methods
Authors: name: Sara Ghazanfari, name: Wei-An Lin, name: Haitong Tian, name: Ersin Yumer

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.