Progressive3D: Progressively Local Editing for Text-to-3D Content
  Creation with Complex Semantic Prompts
Authors: name: Xinhua Cheng, name: Tianyu Yang, name: Jianan Wang, name: Yu Li, name: Lei Zhang, name: Jian Zhang, name: Li Yuan

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.