Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era
Authors: name: Chenghao Li, name: Chaoning Zhang, name: Atish Waghwase, name: Lik-Hang Lee, name: Francois Rameau, name: Yang Yang, name: Sung-Ho Bae, name: Choong Seon Hong

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.