Papers: arxiv:2305.06131

Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era

Chenghao Li ,
Chaoning Zhang ,
Atish Waghwase ,
Lik-Hang Lee ,
Francois Rameau ,
Yang Yang ,
Sung-Ho Bae ,
Choong Seon Hong
·published on May 10


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.


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