  3D content creation plays a vital role in various applications, such as
gaming, robotics simulation, and virtual reality. However, the process is
labor-intensive and time-consuming, requiring skilled designers to invest
considerable effort in creating a single 3D asset. To address this challenge,
text-to-3D generation technologies have emerged as a promising solution for
automating 3D creation. Leveraging the success of large vision language models,
these techniques aim to generate 3D content based on textual descriptions.
Despite recent advancements in this area, existing solutions still face
significant limitations in terms of generation quality and efficiency. In this
survey, we conduct an in-depth investigation of the latest text-to-3D creation
methods. We provide a comprehensive background on text-to-3D creation,
including discussions on datasets employed in training and evaluation metrics
used to assess the quality of generated 3D models. Then, we delve into the
various 3D representations that serve as the foundation for the 3D generation
process. Furthermore, we present a thorough comparison of the rapidly growing
literature on generative pipelines, categorizing them into feedforward
generators, optimization-based generation, and view reconstruction approaches.
By examining the strengths and weaknesses of these methods, we aim to shed
light on their respective capabilities and limitations. Lastly, we point out
several promising avenues for future research. With this survey, we hope to
inspire researchers further to explore the potential of open-vocabulary
text-conditioned 3D content creation.
