SpotEdit: Evaluating Visually-Guided Image Editing Methods

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

Track: Short paper

Keywords: Visually-guided image editing, diffusion models, autoregressive models

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.

Submission Number: 7