Generative Visual Chain-of-Thought for Image Editing

• URL: http://arxiv.org/abs/2603.01893v1
• Date: Mon, 02 Mar 2026 14:12:52 GMT
• Title: Generative Visual Chain-of-Thought for Image Editing
• Authors: Zijin Yin, Tiankai Hang, Yiji Cheng, Shiyi Zhang, Runze He, Yu Xu, Chunyu Wang, Bing Li, Zheng Chang, Kongming Liang, Qinglin Lu, Zhanyu Ma,
• Abstract summary: Existing image editing methods struggle to perceive where to edit, especially under complex scenes and nuanced spatial instructions.<n>To address this issue, we propose Generative Visual Chain-of-Thought (GVCoT)<n>GVCoT performs native visual reasoning by first generating spatial cues to localize the target region and then executing the edit.
• Score: 48.64933075232273
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing image editing methods struggle to perceive where to edit, especially under complex scenes and nuanced spatial instructions. To address this issue, we propose Generative Visual Chain-of-Thought (GVCoT), a unified framework that performs native visual reasoning by first generating spatial cues to localize the target region and then executing the edit. Unlike prior text-only CoT or tool-dependent visual CoT paradigms, GVCoT jointly optimizes visual tokens generated during the reasoning and editing phases in an end-to-end manner. This way fosters the emergence of innate spatial reasoning ability and enables more effective utilization of visual-domain cues. The main challenge of training GCVoT lies in the scarcity of large-scale editing data with precise edit region annotations; to this end, we construct GVCoT-Edit-Instruct, a dataset of 1.8M high-quality samples spanning 19 tasks. We adopt a progressive training strategy: supervised fine-tuning to build foundational localization ability in reasoning trace before final editing, followed by reinforcement learning to further improve reasoning and editing quality. Finally, we introduce SREdit-Bench, a new benchmark designed to comprehensively stress-test models under sophisticated scenes and fine-grained referring expressions. Experiments demonstrate that GVCoT consistently outperforms state-of-the-art models on SREdit-Bench and ImgEdit. We hope our GVCoT will inspire future research toward interpretable and precise image editing.

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