title: DreamCatalyst: Fast and High-Quality 3D Editing via Controlling
    Editability and Identity Preservation
summary: Score distillation sampling (SDS) has emerged as an effective framework in
  text-driven 3D editing tasks due to its inherent 3D consistency. However,
  existing SDS-based 3D editing methods suffer from extensive training time and
  lead to low-quality results, primarily because these methods deviate from the
  sampling dynamics of diffusion models. In this paper, we propose DreamCatalyst,
  a novel framework that interprets SDS-based editing as a diffusion reverse
  process. Our objective function considers the sampling dynamics, thereby making
  the optimization process of DreamCatalyst an approximation of the diffusion
  reverse process in editing tasks. DreamCatalyst aims to reduce training time
  and improve editing quality. DreamCatalyst presents two modes: (1) a faster
  mode, which edits the NeRF scene in only about 25 minutes, and (2) a
  high-quality mode, which produces superior results in less than 70 minutes.
  Specifically, our high-quality mode outperforms current state-of-the-art NeRF
  editing methods both in terms of speed and quality. See more extensive results
  on our project page: https://dream-catalyst.github.io.
paper_authors._id: 6697365bd8d26ae50387b77d
paper_authors.name: Jiwook Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-18T09:06:44.804Z
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paper_authors.fullname: kimjiwook
paper_authors.type: user
paper_authors.user: jiwook919
paper_authors._id: 6697365bd8d26ae50387b77e
paper_authors.name: Seonho Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-18T09:06:48.123Z
paper_authors._id: 6496dd563540608633191869
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paper_authors.fullname: Seonho Lee
paper_authors.type: user
paper_authors.user: Glanceyes
paper_authors._id: 6697365bd8d26ae50387b77f
paper_authors.name: Jaeyo Shin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-27T22:21:04.633Z
paper_authors._id: 64b9f86b760936217a3936d3
paper_authors.avatarUrl: /avatars/c270977c65f6ee9c14dbbabf15568a17.svg
paper_authors.fullname: Jaeyo Shin
paper_authors.type: user
paper_authors.user: j-mayo
paper_authors._id: 6697365bd8d26ae50387b780
paper_authors.name: Jiho Choi
paper_authors._id: 6697365bd8d26ae50387b781
paper_authors.name: Hyunjung Shim
paper_title: DreamCatalyst: Fast and High-Quality 3D Editing via Controlling
    Editability and Identity Preservation
paper_summary: Score distillation sampling (SDS) has emerged as an effective framework in
  text-driven 3D editing tasks due to its inherent 3D consistency. However,
  existing SDS-based 3D editing methods suffer from extensive training time and
  lead to low-quality results, primarily because these methods deviate from the
  sampling dynamics of diffusion models. In this paper, we propose DreamCatalyst,
  a novel framework that interprets SDS-based editing as a diffusion reverse
  process. Our objective function considers the sampling dynamics, thereby making
  the optimization process of DreamCatalyst an approximation of the diffusion
  reverse process in editing tasks. DreamCatalyst aims to reduce training time
  and improve editing quality. DreamCatalyst presents two modes: (1) a faster
  mode, which edits the NeRF scene in only about 25 minutes, and (2) a
  high-quality mode, which produces superior results in less than 70 minutes.
  Specifically, our high-quality mode outperforms current state-of-the-art NeRF
  editing methods both in terms of speed and quality. See more extensive results
  on our project page: https://dream-catalyst.github.io.
paper_ai_summary: DreamCatalyst improves text-driven 3D editing by interpreting score distillation sampling as a diffusion reverse process, offering faster and higher-quality NeRF scene editing.
paper_ai_keywords: score distillation sampling diffusion reverse process NeRF NeRF editing