RePlan: Reasoning-Guided Region Planning for Complex Instruction-Based Image Editing

Tianyuan Qu, Lei Ke, Xiaohang Zhan, Longxiang Tang, Yuqi Liu, Bohao PENG, Bei Yu, Dong Yu, Jiaya Jia

Keywords: Complex Instruction-Based Image Editing, Vision-Language Models, Benchmark, Reinforcement Learning

Abstract: 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 about 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.

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 8780