Text-to-3D Shape Generation
dc.contributor.author | Lee, Hanhung | en_US |
dc.contributor.author | Savva, Manolis | en_US |
dc.contributor.author | Chang, Angel Xuan | en_US |
dc.contributor.editor | Aristidou, Andreas | en_US |
dc.contributor.editor | Macdonnell, Rachel | en_US |
dc.date.accessioned | 2024-04-30T09:02:58Z | |
dc.date.available | 2024-04-30T09:02:58Z | |
dc.date.issued | 2024 | |
dc.description.abstract | Recent 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. | en_US |
dc.description.documenttype | star | |
dc.description.number | 2 | |
dc.description.sectionheaders | State of the Art Reports | |
dc.description.seriesinformation | Computer Graphics Forum | |
dc.description.volume | 43 | |
dc.identifier.doi | 10.1111/cgf.15061 | |
dc.identifier.issn | 1467-8659 | |
dc.identifier.pages | 27 pages | |
dc.identifier.uri | https://doi.org/10.1111/cgf.15061 | |
dc.identifier.uri | https://diglib.eg.org/handle/10.1111/cgf15061 | |
dc.publisher | The Eurographics Association and John Wiley & Sons Ltd. | en_US |
dc.title | Text-to-3D Shape Generation | en_US |
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