PosBridge：用于身份感知图像编辑的多视图位置嵌入迁移
PosBridge: Multi-View Positional Embedding Transplant for Identity-Aware Image Editing
AI总结：
PosBridge是一种高效灵活的图像编辑框架，通过位置嵌入迁移与角点居中布局，实现自定义对象的无缝插入，在结构一致性、保真度和效率上优于主流基线。
摘要：
面向局部主体的图像编辑旨在将用户指定对象无缝集成到目标场景中。随着生成模型规模不断扩大，训练所需的内存与计算成本日益高昂，因此亟需无需训练且可扩展的编辑框架。为此，我们提出PosBridge——一种高效灵活的自定义对象插入框架。该方法的核心组件是位置嵌入迁移，它引导扩散模型忠实地复现参考对象的结构特征；同时，我们引入角点居中布局，将参考图像与背景图像拼接后作为输入送入FLUX.1-Fill模型。在渐进式去噪过程中，位置嵌入迁移被用于引导目标区域的噪声分布向参考对象的噪声分布靠拢。通过这种方式，角点居中布局可有效引导FLUX.1-Fill模型在指定位置合成与身份一致的内容。大量实验表明，PosBridge在结构一致性、外观保真度和计算效率上均优于主流基线方法，展现出其实用价值与广泛应用潜力。
英文摘要：
Localized subject-driven image editing aims to seamlessly integrate user-specified objects into target scenes. As generative models continue to scale, training becomes increasingly costly in terms of memory and computation, highlighting the need for training-free and scalable editing frameworks. To this end, we propose PosBridge--an efficient and flexible framework for inserting custom objects. A key component of our method is positional embedding transplant, which guides the diffusion model to faithfully replicate the structural characteristics of reference objects. Meanwhile, we introduce the Corner Centered Layout, which concatenates reference images and the background image as input to the FLUX.1-Fill model. During progressive denoising, positional embedding transplant is applied to guide the noise distribution in the target region toward that of the reference object. In this way, Corner Centered Layout effectively directs the FLUX.1-Fill model to synthesize identity-consistent content at the desired location. Extensive experiments demonstrate that PosBridge outperforms mainstream baselines in structural consistency, appearance fidelity, and computational efficiency, showcasing its practical value and potential for broad adoption.
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。
