Lee, HanhungSavva, ManolisChang, Angel XuanAristidou, AndreasMacdonnell, Rachel2024-04-302024-04-3020241467-8659https://doi.org/10.1111/cgf.15061https://diglib.eg.org/handle/10.1111/cgf15061Recent years have seen an explosion of work and interest in text-to-3D shape generation. Much of the progress is driven by advances in 3D representations, large-scale pretraining and representation learning for text and image data enabling generative AI models, and differentiable rendering. Computational systems that can perform text-to-3D shape generation have captivated the popular imagination as they enable non-expert users to easily create 3D content directly from text. However, there are still many limitations and challenges remaining in this problem space. In this state-of-the-art report, we provide a survey of the underlying technology and methods enabling text-to-3D shape generation to summarize the background literature. We then derive a systematic categorization of recent work on text-to-3D shape generation based on the type of supervision data required. Finally, we discuss limitations of the existing categories of methods, and delineate promising directions for future work.10.1111/cgf.1506127 pages