DreamCatalyst: Fast and High-Quality 3D Editing via Controlling
  Editability and Identity Preservation
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.
6697365bd8d26ae50387b77d
Jiwook Kim
claimed_verified
2024-07-18T09:06:44.804Z
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kimjiwook
user
jiwook919
6697365bd8d26ae50387b77e
Seonho Lee
claimed_verified
2024-07-18T09:06:48.123Z
6496dd563540608633191869
https://cdn-avatars.huggingface.co/v1/production/uploads/6496dd563540608633191869/LkJbdJXMcWB-yYucGK8rG.jpeg
Seonho Lee
user
Glanceyes
6697365bd8d26ae50387b77f
Jaeyo Shin
claimed_verified
2024-11-27T22:21:04.633Z
64b9f86b760936217a3936d3
/avatars/c270977c65f6ee9c14dbbabf15568a17.svg
Jaeyo Shin
user
j-mayo
6697365bd8d26ae50387b780
Jiho Choi
6697365bd8d26ae50387b781
Hyunjung Shim
DreamCatalyst: Fast and High-Quality 3D Editing via Controlling
  Editability and Identity Preservation
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.
DreamCatalyst improves text-driven 3D editing by interpreting score distillation sampling as a diffusion reverse process, offering faster and higher-quality NeRF scene editing.
score distillation sampling diffusion reverse process NeRF NeRF editing