title: Progressive3D: Progressively Local Editing for Text-to-3D Content
    Creation with Complex Semantic Prompts
summary: 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.
paper_authors._id: 6530c2e2a9390a5ddf95e6dc
paper_authors.name: Xinhua Cheng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-10-19T08:13:56.016Z
paper_authors._id: 645b4026f9d4ec91fdd516ce
paper_authors.avatarUrl: /avatars/fb6556ab973764af5c44980f4e8fcc71.svg
paper_authors.fullname: Xinhua Cheng
paper_authors.type: user
paper_authors.user: cxh0519
paper_authors._id: 6530c2e2a9390a5ddf95e6dd
paper_authors.name: Tianyu Yang
paper_authors._id: 6530c2e2a9390a5ddf95e6de
paper_authors.name: Jianan Wang
paper_authors._id: 6530c2e2a9390a5ddf95e6df
paper_authors.name: Yu Li
paper_authors._id: 6530c2e2a9390a5ddf95e6e0
paper_authors.name: Lei Zhang
paper_authors._id: 6530c2e2a9390a5ddf95e6e1
paper_authors.name: Jian Zhang
paper_authors._id: 6530c2e2a9390a5ddf95e6e2
paper_authors.name: Li Yuan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-04T07:13:34.737Z
paper_authors._id: 66135a5e50350afe76beebce
paper_authors.avatarUrl: /avatars/370a4b83949355feb050c2cb0425c264.svg
paper_authors.fullname: yl2488
paper_authors.type: user
paper_authors.user: yl2488
paper_title: Progressive3D: Progressively Local Editing for Text-to-3D Content
    Creation with Complex Semantic Prompts
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: image diffusion models Progressive3D locally progressive editing region prompts semantic component suppression text-to-3D generation 3D representations