@article{dreambooth2023,
  title = {DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation},
  author = {Ruiz, N. and Li, Y. and Jampani, V. and Pritch, Y. and Rubinstein, M. and Aberman, K.},
  year = {2023},
  journal = {CVPR 2023 (IEEE/CVF)},
  url = {https://arxiv.org/abs/2208.12242},
  abstract = {Ruiz et al. (2022, published CVPR 2023) introduce DreamBooth, a fine-tuning method that binds a unique text identifier to a specific subject by fine-tuning all weights of a diffusion model on a small reference image set, with a prior-preservation loss that prevents language drift. The result allows the subject to be synthesised in arbitrary contexts, poses, and scenes while maintaining identity. DreamBooth was the first practically accessible character-lock-in method and has been widely productised in animation pre-production pipelines for consistent character generation.},
  keywords = {generative-ai, image-generation, character-animation, training-data},
  note = {AI \& Animation Education Knowledge Base}
}