  We present Retrieve in Style (RIS), an unsupervised framework for facial
feature transfer and retrieval on real images. Recent work shows capabilities
of transferring local facial features by capitalizing on the disentanglement
property of the StyleGAN latent space. RIS improves existing art on the
following: 1) Introducing more effective feature disentanglement to allow for
challenging transfers (ie, hair, pose) that were not shown possible in SoTA
methods. 2) Eliminating the need for per-image hyperparameter tuning, and for
computing a catalog over a large batch of images. 3) Enabling fine-grained face
retrieval using disentangled facial features (eg, eyes). To our best knowledge,
this is the first work to retrieve face images at this fine level. 4)
Demonstrating robust, natural editing on real images. Our qualitative and
quantitative analyses show RIS achieves both high-fidelity feature transfers
and accurate fine-grained retrievals on real images. We also discuss the
responsible applications of RIS.
