SwapAnything: Enabling Arbitrary Object Swapping in Personalized Visual
  Editing
Jing Gu Yilin Wang Nanxuan Zhao Wei Xiong Qing Liu Zhifei Zhang He Zhang Jianming Zhang HyunJoon Jung Xin Eric Wang
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.