@article{dreamfusiontextto3d2023,
  title = {DreamFusion: Text-to-3D Using 2D Diffusion},
  author = {Poole, B. and Jain, A. and Barron, J. T. and Mildenhall, B.},
  year = {2023},
  journal = {ICLR 2023},
  url = {https://arxiv.org/abs/2209.14988},
  abstract = {Poole et al. (2022, published ICLR 2023) present DreamFusion, the first method to generate 3D assets from text descriptions by distilling knowledge from a pretrained 2D diffusion model into a NeRF via a novel Score Distillation Sampling loss. The approach requires no 3D training data and generalises to arbitrary text prompts. Recognised as an ICLR 2023 Outstanding Paper, DreamFusion established the text-to-3D category and its Score Distillation Sampling technique directly underlies subsequent tools entering animation and game-art production pipelines.},
  keywords = {generative-ai, 3d-generation, image-generation, curriculum-design},
  note = {AI \& Animation Education Knowledge Base}
}