Effective editing of personal content holds a pivotal role in enabling individuals to express their creativity, weaving captivating narratives within their visual stories, and elevate the overall quality and impact of their visual content. Therefore, in this work, we introduce SwapAnything, a novel framework that can swap any objects in an image with personalized concepts given by the reference, while keeping the context unchanged. Compared with existing methods for personalized subject swapping, SwapAnything has three unique advantages: (1) precise control of arbitrary objects and parts rather than the main subject, (2) more faithful preservation of context pixels, (3) better adaptation of the personalized concept to the image. First, we propose targeted variable swapping to apply region control over latent feature maps and swap masked variables for faithful context preservation and initial semantic concept swapping. Then, we introduce appearance adaptation, to seamlessly adapt the semantic concept into the original image in terms of target location, shape, style, and content during the image generation process. Extensive results on both human and automatic evaluation demonstrate significant improvements of our approach over baseline methods on personalized swapping. Furthermore, SwapAnything shows its precise and faithful swapping abilities across single object, multiple objects, partial object, and cross-domain swapping tasks. SwapAnything also achieves great performance on text-based swapping and tasks beyond swapping such as object insertion.

翻译：个性化内容的高效编辑在激发个体创造力、编织引人入胜的视觉叙事以及提升视觉内容的整体质量与影响力方面扮演着关键角色。为此，本文提出SwapAnything——一种新颖框架，能够在保持图像上下文不变的前提下，将任意对象替换为参考图像提供的个性化概念。与现有个性化主体交换方法相比，SwapAnything具有三大独特优势：（1）可精确控制任意对象及局部区域，而非仅局限于主体；（2）更忠实地保留上下文像素；（3）将个性化概念更完美地适配至目标图像。首先，我们提出目标变量交换技术，对潜特征图实施区域控制，并通过交换掩码变量实现上下文像素的忠实保留与初始语义概念的交换。随后引入外观适配模块，在图像生成过程中将语义概念无缝融入原始图像的目标位置、形状、风格及内容。大量人工与自动评估结果表明，本方法在个性化交换任务上显著优于基线方法。此外，SwapAnything在单对象、多对象、部分对象及跨领域交换任务中均展现出精准且忠实的交换能力，并在基于文本的交换及对象插入等延伸任务中同样表现优异。