Progressive3D: Progressively Local Editing for Text-to-3D Content
  Creation with Complex Semantic Prompts
Recent text-to-3D generation methods achieve impressive 3D content creation
capacity thanks to the advances in image diffusion models and optimizing
strategies. However, current methods struggle to generate correct 3D content
for a complex prompt in semantics, i.e., a prompt describing multiple
interacted objects binding with different attributes. In this work, we propose
a general framework named Progressive3D, which decomposes the entire generation
into a series of locally progressive editing steps to create precise 3D content
for complex prompts, and we constrain the content change to only occur in
regions determined by user-defined region prompts in each editing step.
Furthermore, we propose an overlapped semantic component suppression technique
to encourage the optimization process to focus more on the semantic differences
between prompts. Extensive experiments demonstrate that the proposed
Progressive3D framework generates precise 3D content for prompts with complex
semantics and is general for various text-to-3D methods driven by different 3D
representations.
6530c2e2a9390a5ddf95e6dc
Xinhua Cheng
admin_assigned
2023-10-19T08:13:56.016Z
645b4026f9d4ec91fdd516ce
/avatars/fb6556ab973764af5c44980f4e8fcc71.svg
Xinhua Cheng
user
cxh0519
6530c2e2a9390a5ddf95e6dd
Tianyu Yang
6530c2e2a9390a5ddf95e6de
Jianan Wang
6530c2e2a9390a5ddf95e6df
Yu Li
6530c2e2a9390a5ddf95e6e0
Lei Zhang
6530c2e2a9390a5ddf95e6e1
Jian Zhang
6530c2e2a9390a5ddf95e6e2
Li Yuan
claimed_verified
2025-04-04T07:13:34.737Z
66135a5e50350afe76beebce
/avatars/370a4b83949355feb050c2cb0425c264.svg
yl2488
user
yl2488
Progressive3D: Progressively Local Editing for Text-to-3D Content
  Creation with Complex Semantic Prompts
Recent text-to-3D generation methods achieve impressive 3D content creation
capacity thanks to the advances in image diffusion models and optimizing
strategies. However, current methods struggle to generate correct 3D content
for a complex prompt in semantics, i.e., a prompt describing multiple
interacted objects binding with different attributes. In this work, we propose
a general framework named Progressive3D, which decomposes the entire generation
into a series of locally progressive editing steps to create precise 3D content
for complex prompts, and we constrain the content change to only occur in
regions determined by user-defined region prompts in each editing step.
Furthermore, we propose an overlapped semantic component suppression technique
to encourage the optimization process to focus more on the semantic differences
between prompts. Extensive experiments demonstrate that the proposed
Progressive3D framework generates precise 3D content for prompts with complex
semantics and is general for various text-to-3D methods driven by different 3D
representations.
A Progressive3D framework uses locally progressive editing and semantic component suppression to generate precise 3D content from complex text prompts, improving text-to-3D generation accuracy.
image diffusion models Progressive3D locally progressive editing region prompts semantic component suppression text-to-3D generation 3D representations