FineEdit: 基于边界框引导的细粒度图像编辑
FineEdit: Fine-Grained Image Edit with Bounding Box Guidance
AI总结：
本文提出FineEdit，通过多级边界框注入方法提升图像编辑的精确度与背景一致性，结合120万对精细标注图像数据集和FineEdit-Bench基准测试，验证其在指令遵循和背景保留方面的优越性能。
摘要：
基于扩散模型的图像编辑技术在现实应用中取得了显著进展。然而，传统模型通常依赖自然语言提示，这些提示往往缺乏定位目标物体的精度。因此，这些模型由于其全局图像再生范式而难以保持背景一致性。考虑到视觉线索为用户提供了一种直观的方法来突出特定区域，我们利用边界框作为引导以明确定义编辑目标。这种方法确保扩散模型能够准确定位目标同时保持背景一致性。为此，我们提出了FineEdit，一种多级边界框注入方法，使模型能更有效地利用空间条件。为了支持这种高精度引导，我们提出了FineEdit-1.2M，一个大规模、细粒度的数据集，包含120万对图像编辑对，具有精确的边界框标注。此外，我们构建了一个全面的基准，称为FineEdit-Bench，其中包括10个主题的1000张图像，以有效评估基于区域的编辑能力。在FineEdit-Bench上的评估显示，我们的模型在指令遵循和背景保留方面显著优于最先进的开源模型（例如Qwen-Image-Edit和LongCat-Image-Edit）。进一步在开放基准（GEdit和ImgEdit Bench）上的评估证实了其优越的泛化能力和鲁棒性。
英文摘要：
Diffusion-based image editing models have achieved significant progress in real world applications. However, conventional models typically rely on natural language prompts, which often lack the precision required to localize target objects. Consequently, these models struggle to maintain background consistency due to their global image regeneration paradigm. Recognizing that visual cues provide an intuitive means for users to highlight specific areas of interest, we utilize bounding boxes as guidance to explicitly define the editing target. This approach ensures that the diffusion model can accurately localize the target while preserving background consistency. To achieve this, we propose FineEdit, a multi-level bounding box injection method that enables the model to utilize spatial conditions more effectively. To support this high precision guidance, we present FineEdit-1.2M, a large scale, fine-grained dataset comprising 1.2 million image editing pairs with precise bounding box annotations. Furthermore, we construct a comprehensive benchmark, termed FineEdit-Bench, which includes 1,000 images across 10 subjects to effectively evaluate region based editing capabilities. Evaluations on FineEdit-Bench demonstrate that our model significantly outperforms state-of-the-art open-source models (e.g., Qwen-Image-Edit and LongCat-Image-Edit) in instruction compliance and background preservation. Further assessments on open benchmarks (GEdit and ImgEdit Bench) confirm its superior generalization and robustness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
