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

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arxiv 2305.06131 v4 pith:ORZSIP7W submitted 2023-05-10 cs.CV

Chenghao Li , Chaoning Zhang , Joseph Cho , Atish Waghwase , Lik-Hang Lee , Francois Rameau , Yang Yang , Sung-Ho Bae
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Choong Seon Hong
This is my paper · ORCID
classification cs.CV
keywords text-to-3dgenerationfieldincludingresearchaigccontentdata
verification ladder T0 review T1 audit T2 compute T3 formal
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Generative AI has made significant progress in recent years, with text-guided content generation being the most practical as it facilitates interaction between human instructions and AI-generated content (AIGC). Thanks to advancements in text-to-image and 3D modeling technologies, like neural radiance field (NeRF), text-to-3D has emerged as a nascent yet highly active research field. Our work conducts a comprehensive survey on this topic 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 Structured and non-Structured data. Building on this pre-requisite, we introduce various core 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, scene generation and 3D editing. Finally, we discuss the agenda for the future development of text-to-3D.

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Cited by 5 Pith papers

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