title: 3D-Fixup: Advancing Photo Editing with 3D Priors
summary: Despite significant advances in modeling image priors via diffusion models,
  3D-aware image editing remains challenging, in part because the object is only
  specified via a single image. To tackle this challenge, we propose 3D-Fixup, a
  new framework for editing 2D images guided by learned 3D priors. The framework
  supports difficult editing situations such as object translation and 3D
  rotation. To achieve this, we leverage a training-based approach that harnesses
  the generative power of diffusion models. As video data naturally encodes
  real-world physical dynamics, we turn to video data for generating training
  data pairs, i.e., a source and a target frame. Rather than relying solely on a
  single trained model to infer transformations between source and target frames,
  we incorporate 3D guidance from an Image-to-3D model, which bridges this
  challenging task by explicitly projecting 2D information into 3D space. We
  design a data generation pipeline to ensure high-quality 3D guidance throughout
  training. Results show that by integrating these 3D priors, 3D-Fixup
  effectively supports complex, identity coherent 3D-aware edits, achieving
  high-quality results and advancing the application of diffusion models in
  realistic image manipulation. The code is provided at
  https://3dfixup.github.io/
paper_authors._id: 6826a515cf1804ab4c37cde8
paper_authors.name: Yen-Chi Cheng
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-16T18:15:41.266Z
paper_authors._id: 634cbe8888c6b621d3577fe6
paper_authors.avatarUrl: /avatars/e8108ccb3602a5e387a03aaa06d7fdc3.svg
paper_authors.fullname: Yen-Chi Cheng
paper_authors.type: user
paper_authors.user: yenchic
paper_authors._id: 6826a515cf1804ab4c37cde9
paper_authors.name: Krishna Kumar Singh
paper_authors._id: 6826a515cf1804ab4c37cdea
paper_authors.name: Jae Shin Yoon
paper_authors._id: 6826a515cf1804ab4c37cdeb
paper_authors.name: Alex Schwing
paper_authors._id: 6826a515cf1804ab4c37cdec
paper_authors.name: Liangyan Gui
paper_authors._id: 6826a515cf1804ab4c37cded
paper_authors.name: Matheus Gadelha
paper_authors._id: 6826a515cf1804ab4c37cdee
paper_authors.name: Paul Guerrero
paper_authors._id: 6826a515cf1804ab4c37cdef
paper_authors.name: Nanxuan Zhao
paper_title: 3D-Fixup: Advancing Photo Editing with 3D Priors
paper_summary: Despite significant advances in modeling image priors via diffusion models,
  3D-aware image editing remains challenging, in part because the object is only
  specified via a single image. To tackle this challenge, we propose 3D-Fixup, a
  new framework for editing 2D images guided by learned 3D priors. The framework
  supports difficult editing situations such as object translation and 3D
  rotation. To achieve this, we leverage a training-based approach that harnesses
  the generative power of diffusion models. As video data naturally encodes
  real-world physical dynamics, we turn to video data for generating training
  data pairs, i.e., a source and a target frame. Rather than relying solely on a
  single trained model to infer transformations between source and target frames,
  we incorporate 3D guidance from an Image-to-3D model, which bridges this
  challenging task by explicitly projecting 2D information into 3D space. We
  design a data generation pipeline to ensure high-quality 3D guidance throughout
  training. Results show that by integrating these 3D priors, 3D-Fixup
  effectively supports complex, identity coherent 3D-aware edits, achieving
  high-quality results and advancing the application of diffusion models in
  realistic image manipulation. The code is provided at
  https://3dfixup.github.io/
paper_ai_summary: A new framework, 3D-Fixup, improves 3D-aware image editing by integrating 3D priors learned from video data into diffusion models.
paper_ai_keywords: 3D-Fixup diffusion models 3D priors Image-to-3D model 3D guidance data generation pipeline 3D-aware edits realistic image manipulation