Generative AI (AIGC, a.k.a. AI generated content) has made remarkable progress in the past few years, among which text-guided content generation is the most practical one since it enables the interaction between human instruction and AIGC. Due to the development in text-to-image as well 3D modeling technologies (like NeRF), text-to-3D has become a newly emerging yet highly active research field. Our work conducts the first yet comprehensive survey on text-to-3D to help readers interested in this direction quickly catch up with its fast development. First, we introduce 3D data representations, including both Euclidean data and non-Euclidean data. On top of that, we introduce various foundation technologies as well as summarize how recent works combine those foundation technologies to realize satisfactory text-to-3D. Moreover, we summarize how text-to-3D technology is used in various applications, including avatar generation, texture generation, shape transformation, and scene generation.

翻译：生成式AI（AIGC，即人工智能生成内容）在过去几年取得了显著进展，其中文本引导的内容生成最具实用性，因为它实现了人类指令与AIGC之间的交互。得益于文本到图像技术以及三维建模技术（如NeRF）的发展，文本到三维已成为一个新兴且高度活跃的研究领域。本文首次对文本到三维技术进行系统性综述，旨在帮助对这一方向感兴趣的读者快速跟进其快速发展。首先，我们介绍三维数据表示，包括欧几里得数据和非欧几里得数据。在此基础上，我们介绍各种基础技术，并总结近期工作如何将这些基础技术相结合以实现令人满意的文本到三维效果。此外，我们总结了文本到三维技术在各类应用中的使用情况，包括虚拟形象生成、纹理生成、形状变换和场景生成。