Inter-Edit: First Benchmark for Interactive Instruction-Based Image Editing
Liu, Delong, Hou, Haotian, Hou, Zhaohui, Huang, Zhiyuan, Han, Shihao, Zhan, Mingjie, Zhao, Zhicheng, Su, Fei
原文摘要
Precise and controllable image editing remains a significant challenge. Current methods often rely on text prompts, but achieving accurate spatial localization solely through descriptions is inherently difficult. Mask-based approaches, though offering better control, typically require overly precise user annotations, thus increasing user burden and leading to unnatural results. To bridge this gap, we introduce the **I**nteractive **I**nstruction-based **I**mage **E**diting (I^3E) task, which generates high-quality edits from a more intuitive combination: concise text instructions and imprecise spatial guidance. To address the critical lack of suitable data, we propose an efficient pipeline to generate Inter-Edit, a new million-scale training dataset that simulates realistic user masks---not strictly segment-aligned. We also present a comprehensive benchmark, featuring a meticulously human-annotated test set that captures diverse, localization-dependent editing scenarios and realistic user interaction patterns. To evaluate this task, we introduce a new suite of position-aware metrics that strongly correlate with human perceptual judgments. Finally, we develop three baseline models trained on Inter-Edit. Extensive experiments demonstrate that our methods significantly enhance I_3E performance, achieving substantial improvements in localization and edit quality, and outperforming existing state-of-the-art models. The Inter-Edit dataset and all related code are publicly available at https://github.com/Delong-liu-bupt/Inter-Edit.
cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表