title: Customize your NeRF: Adaptive Source Driven 3D Scene Editing via
    Local-Global Iterative Training
summary: In this paper, we target the adaptive source driven 3D scene editing task by
  proposing a CustomNeRF model that unifies a text description or a reference
  image as the editing prompt. However, obtaining desired editing results
  conformed with the editing prompt is nontrivial since there exist two
  significant challenges, including accurate editing of only foreground regions
  and multi-view consistency given a single-view reference image. To tackle the
  first challenge, we propose a Local-Global Iterative Editing (LGIE) training
  scheme that alternates between foreground region editing and full-image
  editing, aimed at foreground-only manipulation while preserving the background.
  For the second challenge, we also design a class-guided regularization that
  exploits class priors within the generation model to alleviate the
  inconsistency problem among different views in image-driven editing. Extensive
  experiments show that our CustomNeRF produces precise editing results under
  various real scenes for both text- and image-driven settings.
paper_authors._id: 656e9f847e3e612b355b9b51
paper_authors.name: Runze He
paper_authors._id: 656e9f847e3e612b355b9b52
paper_authors.name: Shaofei Huang
paper_authors._id: 656e9f847e3e612b355b9b53
paper_authors.name: Xuecheng Nie
paper_authors._id: 656e9f847e3e612b355b9b54
paper_authors.name: Tianrui Hui
paper_authors._id: 656e9f847e3e612b355b9b55
paper_authors.name: Luoqi Liu
paper_authors._id: 656e9f847e3e612b355b9b56
paper_authors.name: Jiao Dai
paper_authors._id: 656e9f847e3e612b355b9b57
paper_authors.name: Jizhong Han
paper_authors._id: 656e9f847e3e612b355b9b58
paper_authors.name: Guanbin Li
paper_authors._id: 656e9f847e3e612b355b9b59
paper_authors.name: Si Liu
paper_title: Customize your NeRF: Adaptive Source Driven 3D Scene Editing via
    Local-Global Iterative Training
paper_summary: In this paper, we target the adaptive source driven 3D scene editing task by
  proposing a CustomNeRF model that unifies a text description or a reference
  image as the editing prompt. However, obtaining desired editing results
  conformed with the editing prompt is nontrivial since there exist two
  significant challenges, including accurate editing of only foreground regions
  and multi-view consistency given a single-view reference image. To tackle the
  first challenge, we propose a Local-Global Iterative Editing (LGIE) training
  scheme that alternates between foreground region editing and full-image
  editing, aimed at foreground-only manipulation while preserving the background.
  For the second challenge, we also design a class-guided regularization that
  exploits class priors within the generation model to alleviate the
  inconsistency problem among different views in image-driven editing. Extensive
  experiments show that our CustomNeRF produces precise editing results under
  various real scenes for both text- and image-driven settings.
paper_ai_summary: A CustomNeRF model addresses challenges in 3D scene editing by using text or image prompts, with a Local-Global Iterative Editing scheme and class-guided regularization to improve accuracy and consistency.
paper_ai_keywords: CustomNeRF Local-Global Iterative Editing (LGIE) class-guided regularization