Belangrijkste concepten
This paper surveys the rapidly developing field of text-to-3D generation, exploring core technologies, seminal methods, enhancement directions, and applications, ultimately highlighting its potential to revolutionize 3D content creation.
Samenvatting
Bibliographic Information:
Li, C., Zhang, C., Cho, J., Waghwase, A., Lee, L., Rameau, F., ... & Hong, C. S. (2024). Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era. Journal of LaTeX Class Files, 14(8), 1-10.
Research Objective:
This paper provides a comprehensive overview of the current state-of-the-art in text-to-3D generation technologies, focusing on their evolution, core techniques, key challenges, and potential applications.
Methodology:
The authors conduct a comprehensive literature review of relevant research in text-to-3D generation, categorizing and analyzing existing methods based on their underlying techniques, strengths, limitations, and areas for improvement.
Key Findings:
- Text-to-3D generation has witnessed significant advancements, driven by the progress in generative AI, particularly in neural rendering, diffusion models, and text-image synthesis.
- The integration of neural radiance fields (NeRF) with pre-trained text-to-image diffusion models has emerged as a dominant paradigm for high-quality text-to-3D generation.
- Key challenges include improving fidelity, efficiency, consistency, controllability, and diversity of generated 3D models.
- Text-to-3D technology finds applications in diverse fields, including avatar generation, scene generation, texture generation, and 3D editing.
Main Conclusions:
Text-to-3D generation holds immense potential to democratize 3D content creation, enabling users to generate complex and realistic 3D models from natural language descriptions. The authors highlight key research directions to address current limitations and further advance the field.
Significance:
This survey provides a valuable resource for researchers and practitioners interested in understanding the current landscape and future directions of text-to-3D generation, a technology poised to revolutionize various industries reliant on 3D content.
Limitations and Future Research:
- The survey primarily focuses on text-driven generation, leaving other modalities like sketches or audio as potential avenues for future exploration.
- Further research is needed to develop robust evaluation metrics for text-to-3D generation, enabling objective comparisons and benchmarking of different methods.
Statistieken
Even at a resolution of just 64×64, DreamFusion requires significant processing time.
DreamPropeller achieves up to a 4.7x speedup in any text-to-3D pipeline based on score distillation.
Citaten
"The accomplishment of Generative AI in the field of text-to-image [2] is quite remarkable."
"Given the 3D nature of our environment, we can understand the need to extend this technology to the 3D domain [5]"