@article{imagetoimagetranslation2017,
  title = {Image-to-Image Translation: pix2pix and CycleGAN},
  author = {Isola, P. and Zhu, J.-Y. and Zhou, T. and Efros, A. A.},
  year = {2017},
  journal = {CVPR 2017 / ICCV 2017 (IEEE/CVF)},
  url = {https://arxiv.org/abs/1611.07004},
  abstract = {Isola et al. (pix2pix, CVPR 2017) and Zhu et al. (CycleGAN, ICCV 2017) established the two canonical approaches to image-to-image translation. pix2pix trains a conditional GAN on aligned image pairs to learn a mapping between visual domains, such as edge maps to photographs or sketches to coloured artwork. CycleGAN removes the paired-data requirement by enforcing cycle consistency, learning to translate between domains from unaligned collections and enabling applications such as photo-to-anime and style-domain transfer. Together these papers underlie sketch colourisation, edge-to-image generation and concept-to-frame pipelines in animation production, and they are the paired-training ancestor that ControlNet explicitly cites. Both are foundational content in AI and animation courses.},
  keywords = {generative-ai, image-generation},
  note = {AI \& Animation Education Knowledge Base}
}