title: NeRF Analogies: Example-Based Visual Attribute Transfer for NeRFs
summary: A Neural Radiance Field (NeRF) encodes the specific relation of 3D geometry
  and appearance of a scene. We here ask the question whether we can transfer the
  appearance from a source NeRF onto a target 3D geometry in a semantically
  meaningful way, such that the resulting new NeRF retains the target geometry
  but has an appearance that is an analogy to the source NeRF. To this end, we
  generalize classic image analogies from 2D images to NeRFs. We leverage
  correspondence transfer along semantic affinity that is driven by semantic
  features from large, pre-trained 2D image models to achieve multi-view
  consistent appearance transfer. Our method allows exploring the mix-and-match
  product space of 3D geometry and appearance. We show that our method
  outperforms traditional stylization-based methods and that a large majority of
  users prefer our method over several typical baselines.
paper_authors._id: 65cc31c628f6e6716cb60009
paper_authors.name: Michael Fischer
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-15T09:44:35.475Z
paper_authors._id: 65cdd1f42f25d72f998744ed
paper_authors.avatarUrl: /avatars/29030f71078de3a8f6aee541eb59af44.svg
paper_authors.fullname: Michael Fischer
paper_authors.type: user
paper_authors.user: mfai
paper_authors._id: 65cc31c628f6e6716cb6000a
paper_authors.name: Zhengqin Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-14T12:47:11.769Z
paper_authors._id: 64ff5b2569219ce3e47893f1
paper_authors.avatarUrl: /avatars/36bfb3de85488c0efa75d741eac9c40e.svg
paper_authors.fullname: Li
paper_authors.type: user
paper_authors.user: Zhengqin
paper_authors._id: 65cc31c628f6e6716cb6000b
paper_authors.name: Thu Nguyen-Phuoc
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-14T12:47:18.106Z
paper_authors._id: 6304b663c2f4f2d4929ad200
paper_authors.avatarUrl: /avatars/805d4dab512fc53d8bd6bcb60db3821b.svg
paper_authors.fullname: Thu Nguyen-Phuoc
paper_authors.type: user
paper_authors.user: TNP23
paper_authors._id: 65cc31c628f6e6716cb6000c
paper_authors.name: Aljaz Bozic
paper_authors._id: 65cc31c628f6e6716cb6000d
paper_authors.name: Zhao Dong
paper_authors._id: 65cc31c628f6e6716cb6000e
paper_authors.name: Carl Marshall
paper_authors._id: 65cc31c628f6e6716cb6000f
paper_authors.name: Tobias Ritschel
paper_title: NeRF Analogies: Example-Based Visual Attribute Transfer for NeRFs
paper_summary: A Neural Radiance Field (NeRF) encodes the specific relation of 3D geometry
  and appearance of a scene. We here ask the question whether we can transfer the
  appearance from a source NeRF onto a target 3D geometry in a semantically
  meaningful way, such that the resulting new NeRF retains the target geometry
  but has an appearance that is an analogy to the source NeRF. To this end, we
  generalize classic image analogies from 2D images to NeRFs. We leverage
  correspondence transfer along semantic affinity that is driven by semantic
  features from large, pre-trained 2D image models to achieve multi-view
  consistent appearance transfer. Our method allows exploring the mix-and-match
  product space of 3D geometry and appearance. We show that our method
  outperforms traditional stylization-based methods and that a large majority of
  users prefer our method over several typical baselines.
paper_ai_keywords: Neural Radiance Field (NeRF) 3D geometry appearance transfer correspondence transfer semantic affinity pre-trained 2D image models multi-view consistent mix-and-match product space