Text-to-3D Shape Generation
<|reference_start|>Text-to-3D Shape Generation: 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.<|reference_end|>
@article{lee2024text-to-3d,
    title={Text-to-3D Shape Generation},
    author={Han-Hung Lee, Manolis Savva, Angel X. Chang},
    journal={arXiv preprint arXiv:2403.13289},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13289},
    primaryClass={cs.CV}
}