title: Photoswap: Personalized Subject Swapping in Images
summary: In an era where images and visual content dominate our digital landscape, the
  ability to manipulate and personalize these images has become a necessity.
  Envision seamlessly substituting a tabby cat lounging on a sunlit window sill
  in a photograph with your own playful puppy, all while preserving the original
  charm and composition of the image. We present Photoswap, a novel approach that
  enables this immersive image editing experience through personalized subject
  swapping in existing images. Photoswap first learns the visual concept of the
  subject from reference images and then swaps it into the target image using
  pre-trained diffusion models in a training-free manner. We establish that a
  well-conceptualized visual subject can be seamlessly transferred to any image
  with appropriate self-attention and cross-attention manipulation, maintaining
  the pose of the swapped subject and the overall coherence of the image.
  Comprehensive experiments underscore the efficacy and controllability of
  Photoswap in personalized subject swapping. Furthermore, Photoswap
  significantly outperforms baseline methods in human ratings across subject
  swapping, background preservation, and overall quality, revealing its vast
  application potential, from entertainment to professional editing.
paper_authors._id: 64756ca0a51711a3b5915bcb
paper_authors.name: Jing Gu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-30T09:10:49.371Z
paper_authors._id: 634781fae5c0717e6739bc59
paper_authors.avatarUrl: /avatars/2b6d2d0625741b28f326459acad55b69.svg
paper_authors.fullname: Jing Gu
paper_authors.type: user
paper_authors.user: jinggu
paper_authors._id: 64756ca0a51711a3b5915bcc
paper_authors.name: Yilin Wang
paper_authors._id: 64756ca0a51711a3b5915bcd
paper_authors.name: Nanxuan Zhao
paper_authors._id: 64756ca0a51711a3b5915bce
paper_authors.name: Tsu-Jui Fu
paper_authors._id: 64756ca0a51711a3b5915bcf
paper_authors.name: Wei Xiong
paper_authors._id: 64756ca0a51711a3b5915bd0
paper_authors.name: Qing Liu
paper_authors._id: 64756ca0a51711a3b5915bd1
paper_authors.name: Zhifei Zhang
paper_authors._id: 64756ca0a51711a3b5915bd2
paper_authors.name: He Zhang
paper_authors._id: 64756ca0a51711a3b5915bd3
paper_authors.name: Jianming Zhang
paper_authors._id: 64756ca0a51711a3b5915bd4
paper_authors.name: HyunJoon Jung
paper_authors._id: 64756ca0a51711a3b5915bd5
paper_authors.name: Xin Eric Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-09T18:39:16.496Z
paper_authors._id: 64679a226192d39142245e5e
paper_authors.avatarUrl: /avatars/05abee0b6317f100923936ca2099e9eb.svg
paper_authors.fullname: Xin Eric Wang
paper_authors.type: user
paper_authors.user: xw-eric
paper_title: Photoswap: Personalized Subject Swapping in Images
paper_summary: In an era where images and visual content dominate our digital landscape, the
  ability to manipulate and personalize these images has become a necessity.
  Envision seamlessly substituting a tabby cat lounging on a sunlit window sill
  in a photograph with your own playful puppy, all while preserving the original
  charm and composition of the image. We present Photoswap, a novel approach that
  enables this immersive image editing experience through personalized subject
  swapping in existing images. Photoswap first learns the visual concept of the
  subject from reference images and then swaps it into the target image using
  pre-trained diffusion models in a training-free manner. We establish that a
  well-conceptualized visual subject can be seamlessly transferred to any image
  with appropriate self-attention and cross-attention manipulation, maintaining
  the pose of the swapped subject and the overall coherence of the image.
  Comprehensive experiments underscore the efficacy and controllability of
  Photoswap in personalized subject swapping. Furthermore, Photoswap
  significantly outperforms baseline methods in human ratings across subject
  swapping, background preservation, and overall quality, revealing its vast
  application potential, from entertainment to professional editing.
paper_ai_keywords: diffusion models self-attention cross-attention visual concept image editing subject swapping personalized subject swapping visual coherence background preservation human ratings