Progressive3D：逐步局部编辑用于具有复杂语义提示的文本到3D内容创作

摘要

最近的文本到三维生成方法通过图像扩散模型和优化策略的进展取得了令人印象深刻的三维内容创作能力。然而，当前方法在为语义复杂的提示生成正确的三维内容方面存在困难，即描述多个相互作用对象并具有不同属性的提示。在本研究中，我们提出了一个名为Progressive3D的通用框架，将整个生成过程分解为一系列局部渐进编辑步骤，以为复杂提示创建精确的三维内容，并将内容更改限制为仅发生在每个编辑步骤中由用户定义的区域提示确定的区域。此外，我们提出了一种重叠语义组件抑制技术，以鼓励优化过程更多地关注提示之间的语义差异。大量实验证明，所提出的Progressive3D框架为具有复杂语义的提示生成精确的三维内容，并且适用于由不同三维表示驱动的各种文本到三维方法。

English

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.

Progressive3D：逐步局部编辑用于具有复杂语义提示的文本到3D内容创作

Progressive3D: Progressively Local Editing for Text-to-3D Content Creation with Complex Semantic Prompts

摘要

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