@article{animatediff2024,
  title = {AnimateDiff: Animate Your Personalized Text-to-Image Models without Specific Tuning},
  author = {Guo, Y. and Yang, C. and Rao, A. and Liang, Z. and Wang, Y. and Qiao, Y. and Agrawala, M. and Lin, D. and Dai, B.},
  year = {2024},
  journal = {ICLR 2024 (spotlight)},
  url = {https://arxiv.org/abs/2307.04725},
  abstract = {AnimateDiff, accepted as a spotlight at ICLR 2024, introduces a plug-in motion module that can animate any personalised Stable Diffusion model without requiring model-specific retraining. By separating appearance (handled by the base model) from motion (handled by the shared motion module), it enables character-consistent generated animation with a wide range of visual styles. The framework became the entry-point architecture for AI video in animation curricula and underpins thousands of node-based workflows used in teaching labs globally.},
  keywords = {generative-ai, video-generation, motion-synthesis, character-animation, production-practice},
  note = {AI \& Animation Education Knowledge Base}
}