  Generative AI (AIGC, a.k.a. AI generated content) has made significant
progress in recent years, with text-guided content generation being the most
practical as it facilitates interaction between human instructions and AIGC.
Due to advancements in text-to-image and 3D modeling technologies (like NeRF),
text-to-3D has emerged as a nascent yet highly active research field. Our work
conducts the first comprehensive survey and follows up on subsequent research
progress in the overall field, aiming to help readers interested in this
direction quickly catch up with its rapid development. First, we introduce 3D
data representations, including both Euclidean and non-Euclidean data. Building
on this foundation, we introduce various foundational technologies and
summarize how recent work combines these foundational technologies to achieve
satisfactory text-to-3D results. Additionally, we present mainstream baselines
and research directions in recent text-to-3D technology, including fidelity,
efficiency, consistency, controllability, diversity, and applicability.
Furthermore, we summarize the usage of text-to-3D technology in various
applications, including avatar generation, texture generation, shape editing,
and scene generation.
