用双向形变+修复，让拖拽编辑变实时精准
Inpaint4Drag: Repurposing Inpainting Models for Drag-Based Image Editing via Bidirectional Warping
- 将拖拽操作拆解为像素级双向形变与图像修复
- 512x512下形变预览0.01秒，修复仅需0.3秒
- 兼容任意修复模型，无需修改架构
拖拽式图像编辑已成为直观操控图像的强大范式。然而，现有方法主要依赖生成模型的隐空间操作，导致精度有限、反馈延迟且受模型限制。为此，我们提出Inpaint4Drag，将拖拽编辑分解为像素空间的双向形变与图像修复。受物理世界弹性物体变形启发，将图像区域视为可变形材料，在用户操作下保持自然形状。该方法在512x512分辨率下实现0.01秒形变预览与0.3秒高效修复，显著优于现有需数分钟完成一次编辑的方法。通过将拖拽输入直接转换为标准修复格式，本方法作为通用适配器，无需修改架构即可兼容任意修复模型，并自动继承未来修复技术的所有改进。大量实验表明，该方法在视觉质量与精确控制方面表现优异，同时保持实时性能。
原文摘要 · Abstract (English)
Drag-based image editing has emerged as a powerful paradigm for intuitive image manipulation. However, existing approaches predominantly rely on manipulating the latent space of generative models, leading to limited precision, delayed feedback, and model-specific constraints. Accordingly, we present Inpaint4Drag, a novel framework that decomposes drag-based editing into pixel-space bidirectional warping and image inpainting. Inspired by elastic object deformation in the physical world, we treat image regions as deformable materials that maintain natural shape under user manipulation. Our method achieves real-time warping previews (0.01s) and efficient inpainting (0.3s) at 512x512 resolution, significantly improving the interaction experience compared to existing methods that require minutes per edit. By transforming drag inputs directly into standard inpainting formats, our approach serves as a universal adapter for any inpainting model without architecture modification, automatically inheriting all future improvements in inpainting technology. Extensive experiments demonstrate that our method achieves superior visual quality and precise control while maintaining real-time performance. Project page: https://visual-ai.github.io/inpaint4drag/
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。