Problem
Research questions and friction points this paper is trying to address.
region-specific refinement
local detail restoration
background preservation
image editing
fine-grained details
RefineAnything: Multimodal Region-Specific Refinement for Perfect Local Details
🤖 AI Summary
This work addresses the challenge in image editing where fine-grained local structures—such as text or logos—are prone to detail collapse or unintended background alterations. To tackle this, the authors propose a region-specific refinement approach grounded in multimodal diffusion models, featuring a Focus-and-Refine strategy. This strategy reallocates resolution budgets via a crop-and-resize mechanism to concentrate computational resources on the target region, while integrating hybrid mask inpainting and a boundary consistency loss to ensure high-fidelity local reconstruction and strict preservation of non-edited areas. The study also contributes the Refine-30K dataset and the RefineEval benchmark for evaluation. Experimental results demonstrate that the proposed method significantly outperforms existing techniques on RefineEval, achieving near-perfect background retention alongside high-quality detail recovery.
📝 Abstract
We introduce region-specific image refinement as a dedicated problem setting: given an input image and a user-specified region (e.g., a scribble mask or a bounding box), the goal is to restore fine-grained details while keeping all non-edited pixels strictly unchanged. Despite rapid progress in image generation, modern models still frequently suffer from local detail collapse (e.g., distorted text, logos, and thin structures). Existing instruction-driven editing models emphasize coarse-grained semantic edits and often either overlook subtle local defects or inadvertently change the background, especially when the region of interest occupies only a small portion of a fixed-resolution input. We present RefineAnything, a multimodal diffusion-based refinement model that supports both reference-based and reference-free refinement. Building on a counter-intuitive observation that crop-and-resize can substantially improve local reconstruction under a fixed VAE input resolution, we propose Focus-and-Refine, a region-focused refinement-and-paste-back strategy that improves refinement effectiveness and efficiency by reallocating the resolution budget to the target region, while a blended-mask paste-back guarantees strict background preservation. We further introduce a boundary-aware Boundary Consistency Loss to reduce seam artifacts and improve paste-back naturalness. To support this new setting, we construct Refine-30K (20K reference-based and 10K reference-free samples) and introduce RefineEval, a benchmark that evaluates both edited-region fidelity and background consistency. On RefineEval, RefineAnything achieves strong improvements over competitive baselines and near-perfect background preservation, establishing a practical solution for high-precision local refinement. Project Page: https://limuloo.github.io/RefineAnything/.
Innovation
Methods, ideas, or system contributions that make the work stand out.
region-specific refinement
Focus-and-Refine
Boundary Consistency Loss
multimodal diffusion model
background preservation
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