  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.
