@article{stylegan2019,
  title = {A Style-Based Generator Architecture for GANs (StyleGAN)},
  author = {Karras, T. and Laine, S. and Aila, T.},
  year = {2019},
  journal = {CVPR 2019 (IEEE/CVF)},
  url = {https://arxiv.org/abs/1812.04948},
  abstract = {Karras, Laine and Aila (2019) redesigned the GAN generator to inject learned style vectors at each resolution level via adaptive instance normalisation, replacing the traditional input latent with a mapping network that produces a disentangled intermediate latent space. The architecture gives fine-grained independent control over coarse features such as pose and face shape, mid-level features such as hairstyle, and fine-detail features such as skin texture. StyleGAN set the benchmark for photorealistic synthetic face generation and became the reference architecture for generative character work in animation, games and digital-human pipelines. Its successors (StyleGAN2, StyleGAN3) and the latent-editing methods built on its intermediate space form a widely taught family in AI-art and character-generation courses.},
  keywords = {generative-ai, image-generation, character-animation},
  note = {AI \& Animation Education Knowledge Base}
}