@article{controlnet2023,
  title = {Adding Conditional Control to Text-to-Image Diffusion Models (ControlNet)},
  author = {Zhang, L. and Rao, A. and Agrawala, M.},
  year = {2023},
  journal = {IEEE/CVF International Conference on Computer Vision (ICCV 2023)},
  url = {https://openaccess.thecvf.com/content/ICCV2023/html/Zhang_Adding_Conditional_Control_to_Text-to-Image_Diffusion_Models_ICCV_2023_paper.html},
  abstract = {ControlNet introduces a neural network architecture that adds spatial conditioning inputs (pose skeletons, depth maps, edge maps, and other structural signals) to pretrained text-to-image diffusion models without requiring full retraining. The result is controllable image generation where the spatial layout and character pose can be specified precisely, a capability that has become foundational in AI character design workflows. With over 5,000 citations, ControlNet is a landmark of the generative image field and appears in virtually every node-based workflow taught in animation and concept art courses today.},
  keywords = {generative-ai, image-generation, character-animation, production-practice},
  note = {AI \& Animation Education Knowledge Base}
}