RePlan: Reasoning-guided Region Planning for Complex Instruction-based Image Editing
Instruction-based image editing enables natural-language control over visual modifications, yet existing models falter under Instruction-Visual Complexity (IV-Complexity), where intricate instructions meet cluttered or ambiguous scenes. We introduce RePlan (Region-aligned Planning), a plan-then-execute framework that couples a vision-language planner with a diffusion editor. The planner decomposes instructions via step-by-step reasoning and explicitly grounds them to target regions; the editor then applies changes using a training-free attention-region injection mechanism, enabling precise, parallel multi-region edits without iterative inpainting. To strengthen planning, we apply GRPO-based reinforcement learning using 1K instruction-only examples, yielding substantial gains in reasoning fidelity and format reliability. We further present IV-Edit, a benchmark focused on fine-grained grounding and knowledge-intensive edits. Across IV-Complex settings, RePlan consistently outperforms strong baselines trained on far larger datasets, improving regional precision and overall fidelity. Our project page: https://replan-iv-edit.github.io
6944ce43fbf17e708e1860d8
Tianyuan Qu
claimed_verified
2025-12-19T08:57:33.807Z
66e79b3c1c79fc2e51dc1d60
/avatars/8706336e9e7a417505c9bb32583a662f.svg
QU Tianyuan
TainU
user
TainU
6944ce43fbf17e708e1860d9
Lei Ke
6944ce43fbf17e708e1860da
Xiaohang Zhan
6944ce43fbf17e708e1860db
Longxiang Tang
6944ce43fbf17e708e1860dc
Yuqi Liu
6944ce43fbf17e708e1860dd
Bohao Peng
6944ce43fbf17e708e1860de
Bei Yu
6944ce43fbf17e708e1860df
Dong Yu
6944ce43fbf17e708e1860e0
Jiaya Jia
RePlan: Reasoning-guided Region Planning for Complex Instruction-based Image Editing
Instruction-based image editing enables natural-language control over visual modifications, yet existing models falter under Instruction-Visual Complexity (IV-Complexity), where intricate instructions meet cluttered or ambiguous scenes. We introduce RePlan (Region-aligned Planning), a plan-then-execute framework that couples a vision-language planner with a diffusion editor. The planner decomposes instructions via step-by-step reasoning and explicitly grounds them to target regions; the editor then applies changes using a training-free attention-region injection mechanism, enabling precise, parallel multi-region edits without iterative inpainting. To strengthen planning, we apply GRPO-based reinforcement learning using 1K instruction-only examples, yielding substantial gains in reasoning fidelity and format reliability. We further present IV-Edit, a benchmark focused on fine-grained grounding and knowledge-intensive edits. Across IV-Complex settings, RePlan consistently outperforms strong baselines trained on far larger datasets, improving regional precision and overall fidelity. Our project page: https://replan-iv-edit.github.io
RePlan, a plan-then-execute framework, enhances instruction-based image editing by combining a vision-language planner with a diffusion editor, achieving superior performance in complex and intricate editing tasks using limited data.
instruction-based image editing vision-language planner diffusion editor step-by-step reasoning attention-region injection mechanism GRPO-based reinforcement learning IV-Edit benchmark regional precision overall fidelity