title: DreamEditor: Text-Driven 3D Scene Editing with Neural Fields
summary: Neural fields have achieved impressive advancements in view synthesis and
  scene reconstruction. However, editing these neural fields remains challenging
  due to the implicit encoding of geometry and texture information. In this
  paper, we propose DreamEditor, a novel framework that enables users to perform
  controlled editing of neural fields using text prompts. By representing scenes
  as mesh-based neural fields, DreamEditor allows localized editing within
  specific regions. DreamEditor utilizes the text encoder of a pretrained
  text-to-Image diffusion model to automatically identify the regions to be
  edited based on the semantics of the text prompts. Subsequently, DreamEditor
  optimizes the editing region and aligns its geometry and texture with the text
  prompts through score distillation sampling [29]. Extensive experiments have
  demonstrated that DreamEditor can accurately edit neural fields of real-world
  scenes according to the given text prompts while ensuring consistency in
  irrelevant areas. DreamEditor generates highly realistic textures and geometry,
  significantly surpassing previous works in both quantitative and qualitative
  evaluations.
paper_authors._id: 64991c2b49fa1e7cccc97aad
paper_authors.name: Jingyu Zhuang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-26T08:57:06.210Z
paper_authors._id: 63611112108cc69e687b8ea6
paper_authors.avatarUrl: /avatars/8c96fa784ce747d3c1c4bcb09bccf9c0.svg
paper_authors.fullname: zhuang jingyu
paper_authors.type: user
paper_authors.user: zjy526223908
paper_authors._id: 64991c2b49fa1e7cccc97aae
paper_authors.name: Chen Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-26T08:57:40.815Z
paper_authors._id: 62e9d5af6687b60b9c01240a
paper_authors.avatarUrl: /avatars/3983be5662aa28aefcb18deaf08d7cb1.svg
paper_authors.fullname: Chen Wang
paper_authors.type: user
paper_authors.user: chenwang
paper_authors._id: 64991c2b49fa1e7cccc97aaf
paper_authors.name: Lingjie Liu
paper_authors._id: 64991c2b49fa1e7cccc97ab0
paper_authors.name: Liang Lin
paper_authors._id: 64991c2b49fa1e7cccc97ab1
paper_authors.name: Guanbin Li
paper_title: DreamEditor: Text-Driven 3D Scene Editing with Neural Fields
paper_summary: Neural fields have achieved impressive advancements in view synthesis and
  scene reconstruction. However, editing these neural fields remains challenging
  due to the implicit encoding of geometry and texture information. In this
  paper, we propose DreamEditor, a novel framework that enables users to perform
  controlled editing of neural fields using text prompts. By representing scenes
  as mesh-based neural fields, DreamEditor allows localized editing within
  specific regions. DreamEditor utilizes the text encoder of a pretrained
  text-to-Image diffusion model to automatically identify the regions to be
  edited based on the semantics of the text prompts. Subsequently, DreamEditor
  optimizes the editing region and aligns its geometry and texture with the text
  prompts through score distillation sampling [29]. Extensive experiments have
  demonstrated that DreamEditor can accurately edit neural fields of real-world
  scenes according to the given text prompts while ensuring consistency in
  irrelevant areas. DreamEditor generates highly realistic textures and geometry,
  significantly surpassing previous works in both quantitative and qualitative
  evaluations.
paper_ai_summary: DreamEditor enables controlled editing of neural fields using text prompts through mesh-based representations and score distillation sampling, resulting in realistic and consistent scene modifications.
paper_ai_keywords: neural fields view synthesis scene reconstruction mesh-based neural fields text-to-Image diffusion model text encoder score distillation sampling