Text-to-3D Shape Generation
Han-Hung Lee Manolis Savva Angel X. Chang
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.