Text-to-3D Shape Generation

• URL: http://arxiv.org/abs/2403.13289v1
• Date: Wed, 20 Mar 2024 04:03:44 GMT
• Title: Text-to-3D Shape Generation
• Authors: Han-Hung Lee, Manolis Savva, Angel X. Chang,
• Abstract summary: Computational systems that can perform text-to-3D shape generation have captivated the popular imagination. 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.
• Score: 18.76771062964711
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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