REVIEW 8 cited by
RePlan: Reasoning-guided Region Planning for Complex Instruction-based Image Editing
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
RePlan: Reasoning-guided Region Planning for Complex Instruction-based Image Editing
read the original 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 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 consistency. Our project page: https://replan-iv-edit.github.io
Forward citations
Cited by 8 Pith papers
-
GMO-E$^2$DIT: Grounded Multi-Operation Editing for E-Commerce Images
GMO-E²DIT is an agentic editing framework that decouples VLM-based planning from mask-conditioned rendering and uses reflection to execute multi-operation e-commerce image edits with error recovery.
-
GMO-E$^2$DIT: Grounded Multi-Operation Editing for E-Commerce Images
GMO-E²DIT is an agentic framework that decouples VLM-based edit planning from mask-conditioned rendering using reflection loops for reliable multi-operation e-commerce image editing.
-
Rethinking Scribble-Guided Image Editing: Generalization, Instruction Adherence, and Multi-Tasking
Empirical studies reveal instruction-level generalization as the main bottleneck in scribble-guided editing; three strategies (curriculum, multi-task mosaicking, edit-focused loss) achieve SOTA on VIBE benchmark.
-
DDA-Thinker: Decoupled Dual-Atomic Reinforcement Learning for Reasoning-Driven Image Editing
DDA-Thinker decouples planning from generation and applies dual-atomic RL with checklist-based rewards to boost reasoning in image editing, yielding competitive results on RISE-Bench and KRIS-Bench.
-
Making Image Editing Easier via Adaptive Task Reformulation with Agentic Executions
An MLLM agent reformulates image editing tasks into executable operation sequences to improve reliability on challenging cases across existing generative backbones.
-
Making Image Editing Easier via Adaptive Task Reformulation with Agentic Executions
An MLLM agent that profiles, routes, and reformulates image-editing queries into better-conditioned multi-step operations consistently improves existing editors on hard cases.
-
ProductConsistency: Improving Product Identity Preservation in Instruction-Based Image Editing via SFT and RL
Introduces ProductConsistency dataset, benchmark, and Cyclic Consistency reward to fine-tune image editing models, achieving a 5x reduction in character error rate for product identity preservation.
-
OmniVerifier-M1: Multimodal Meta-Verifier with Explicit Structured Recalibration
OmniVerifier-M1 is a generalist visual verifier using symbolic outputs for meta-verification and decoupled RL to outperform joint optimization for robust verification and agentic self-correction.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.