A Style-Based Generator Architecture for Generative Adversarial Networks

Code

• github.com/NVlabs/stylegan
  OfficialIn papertf★ 0
• github.com/NVlabs/ffhq-dataset
  OfficialIn papernone★ 0
• github.com/open-mmlab/mmgeneration
  pytorch★ 2,016
• github.com/genforce/interfacegan
  tf★ 1,556
• github.com/ShenYujun/InterFaceGAN
  tf★ 1,556
• github.com/cc-hpc-itwm/DeepFakeDetection
  none★ 178
• github.com/SiskonEmilia/StyleGAN-PyTorch
  pytorch★ 113
• github.com/a514514772/hijackgan
  tf★ 49
• github.com/hui-po-wang/hijackgan
  tf★ 49
• github.com/woctezuma/steam-stylegan2
  pytorch★ 33

Abstract

We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces.

Tasks

Benchmark Results

Reproductions