PaperlayerView exact revision on arXiv

Source-linked AI summary

RefineAnything: Multimodal Region-Specific Refinement for Perfect Local Details

Dewei Zhou, You Li, Zongxin Yang, Yi Yang

arXiv:2604.06870v1cs.CV

TL;DR

Modern image-generation and editing models often lose fine local details or alter non-target regions, motivating region-specific refinement with strict background preservation. RefineAnything combines region-aware multimodal refinement with crop-focused processing, blended paste-back, boundary-aware training, dedicated data, and evaluation. On RefineEval, it improves refinement quality and semantic alignment while achieving near-perfect background consistency against competitive baselines.

  • Problem

    Existing instruction-driven editing models often miss subtle local defects and unintentionally change backgrounds, especially when the target region is small.

  • Method

    RefineAnything uses explicit region cues, Focus-and-Refine crop processing, blended-mask paste-back, and Boundary Consistency Loss for reference-based and reference-free refinement.

  • Results

    0.020/0.155 vs. 0.040/0.264 MSE/LPIPS errors and 0.000/0.000/0.9997 MSEbg/LPIPSbg/SSIMbg scores show stronger region fidelity and near-perfect background consistency.

  • Takeaways & Limitations

    RefineAnything provides a practical framework for high-precision local refinement while keeping non-edited regions strictly unchanged.

Abstract

from arXiv · show

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/.

1 Introduction

RefineAnything defines region-specific refinement as restoring local details while strictly preserving non-edited pixels. It combines region-aware conditioning, Focus-and-Refine, boundary-aware training, dedicated data and evaluation, and strong refinement and preservation results.

  • Motivation: Local detail collapse distorts text, logos, and thin structures despite plausible global composition, undermining trust and usability in detail-sensitive applications.The problem is especially damaging in product imagery, advertising, signage, packaging, and UI or infographic content.
  • Problem Setting: Region-specific refinement targets user-specified areas while keeping the rest of the image strictly unchanged.The setting requires simultaneous region accuracy, effective detail recovery, and background preservation.
  • Method: RefineAnything injects scribble or bounding-box region cues into a multimodal editing model for reference-based and reference-free refinement.The model is fine-tuned with explicit region cues to improve user control over the refinement location.
  • Method: Focus-and-Refine crops and upsamples the target region, refines it, and pastes it back with a blended mask to improve local reconstruction and preserve the background.The approach reallocates the fixed input-resolution budget to small regions, where VAE reconstruction loss is more pronounced.
  • Data and Evaluation: Refine-30K contains 30K samples spanning 20K reference-based and 10K reference-free cases, while RefineEval measures edited-region fidelity and background preservation.The dataset uses VLM grounding, SAM-based segmentation, controlled inpainting degradations, and explicit background preservation.
  • Results: 0.020/0.155 vs. 0.040/0.264 MSE/LPIPS errors and 0.000/0.000/0.9997 MSEbg/LPIPSbg/SSIMbg scores demonstrate stronger region fidelity and near-perfect background consistency.On RefineEval, semantic alignment and SSIM also improve to 0.793/0.885/0.591 vs. 0.675/0.807/0.436 for DINO/CLIP/SSIM.

2 Related Work

Image generation has progressed from latent diffusion and UNet systems toward scalable Diffusion Transformers and multimodal generators. However, even state-of-the-art models continue to struggle with fine-grained local details.

  • Image Generation Models: Latent diffusion uses a VAE to map images into a compact latent space, accelerating training and sampling.The Stable Diffusion family popularized this VAE-based latent framework.
  • Image Generation Models: Diffusion research has moved from UNet backbones toward better-scaling Diffusion Transformers such as Hunyuan-DiT, PixArt, SD3, and FLUX.These models build on the latent-diffusion foundation.
  • Multimodal Generation: Multimodal generators incorporate VLM encoders to jointly interpret text and images, broadening real-world applications.Qwen-Image and Flux Klein are cited examples using VLM-based multimodal interpretation.
  • Open Challenge: Even state-of-the-art image generation models still struggle with fine-grained local details.

3 Method

RefineAnything combines region-aware multimodal conditioning with focused crop-based generation to recover local details while preserving the rest of the image. Its Focus-and-Refine pipeline crops and enlarges the target, refines it, then pastes it back with boundary-aware blending and supervision.

  • Architecture: RefineAnything accepts an input image, an optional reference image, a scribble region cue, and a text instruction for localized refinement.
  • Architecture: A frozen Qwen2.5-VL encoder converts images, region cues, and instructions into multimodal conditioning tokens for the diffusion denoiser.
  • Architecture: VAE latents from the input and optional reference image provide low-level visual context alongside noisy target latents and multimodal tokens.
  • Focus-and-Refine: Focus-and-Refine expands the region bounding box with a margin, crops the image and mask, resizes them, and performs spatially conditioned generation on the focused view.
  • Focus-and-Refine: Crop-and-resize improves local reconstruction under a fixed resolution by directing capacity and attention toward the target region without adding effective pixel information.
  • Paste-back and loss: The refined crop is composited with the original crop using a dilated and blurred mask, then pasted back; boundary-weighted training further emphasizes seam consistency.

4 Refine-30K Dataset

Refine-30K provides reference-based and reference-free refinement samples, pairing localized degradation and spatial cues with instructions while preserving non-edited content.

  • Dataset Composition: Refine-30K contains 30K training samples: 20K reference-based pairs and 10K instruction-only reference-free samples.Reference-based samples provide refinement instructions and reference images; reference-free samples use refinement text alone.
  • Reference-Based Data: Reference-based samples convert image pairs into supervised tuples containing a degraded input, reference image, target image, spatial mask, and instruction.The pipeline uses VLM grounding to identify salient subjects and localize the refinement goal.
  • Reference-Based Data: Controlled local corruptions and light paste-back blending ensure that the degraded input differs from the target only within the edited region.This design explicitly preserves the surrounding content during data construction.
  • Reference-Free Data: Reference-free samples use a single image, a spatial cue, and a refinement instruction, with VLM filtering to remove implausible or semantically inconsistent degradations.The original image serves as ground truth for the synthesized degraded input.

5 Experiment

RefineEval tests edited-region fidelity and background preservation in reference-based and reference-free settings, where RefineAnything outperforms competitive baselines and preserves backgrounds nearly perfectly.

  • 5 Experiment: RefineEval covers reference-based identity-sensitive content and reference-free common structures, with localized regions, instructions, and clean target images.Reference-based cases include logos, products, and person identities; reference-free cases include bodies, objects, faces, and text.
  • 5.1 Benchmarks: Reference-based evaluation measures edited-region fidelity against ground truth and background preservation against the input using reconstruction, perceptual, feature, and structural metrics.The reported metrics include MSE, SSIM, LPIPS, VGG, DINO, CLIP, MSEbg, LPIPSbg, and SSIMbg.
  • 5.4 Quantitative Results: RefineAnything ranks first across all five reference-free subjective criteria, surpassing Qwen-Edit by +0.725, +0.758, +0.771, +0.745, and +0.430.The criteria are VQ, Nat., Aes., Det., and Faith., respectively.
  • 5.4 Quantitative Results: MSEbg = 0.000, LPbg = 0.000, and SSIMbg = 0.9997 demonstrate near-perfect background consistency in reference-based refinement.The corresponding Kontext values are MSEbg = 0.011, LPbg = 0.019, and SSIMbg = 0.9660.
  • 5.5 Qualitative Results: Qualitative comparisons report stronger subtle-detail recovery and strict background preservation than prior methods in both refinement settings.Prior methods are described as suffering from background changes, weak instruction or reference responsiveness, and limited fine-detail recovery.
  • 5.6 Ablation Study: Removing Focus-and-Refine weakens local refinement and can leave errors or introduce artifacts, while removing Boundary Consistency Loss harms coherence at the paste-back boundary.The ablations connect focusing with sharper local details and the boundary loss with improved local-to-background coherence.

6 Conclusion

RefineAnything targets high-precision local refinement by improving fine-grained details while keeping non-edited regions unchanged through focused processing and seamless paste-back.

  • 6 Conclusion: RefineAnything combines Focus-and-Refine, blended-mask paste-back, and Boundary Consistency Loss for region-specific refinement.The framework supports reference-based and reference-free settings through Refine-30K and RefineEval.
  • 6 Conclusion: Extensive experiments show improved local detail fidelity and semantic alignment with near-perfect background preservation.The conclusion presents this as a practical approach for high-precision image generation and editing workflows.
Loading 2604.06870v1…