We present Retrieve in Style (RIS), an unsupervised framework for fine-grained facial feature transfer and retrieval on real images. Recent work shows that it is possible to learn a catalog that allows local semantic transfers of facial features on generated images by capitalizing on the disentanglement property of the StyleGAN latent space. RIS improves existing art on: 1) feature disentanglement and allows for challenging transfers (i.e., hair and 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 face retrieval using the proposed facial features (e.g., eyes), and to our best knowledge, is the first work to retrieve face images at the fine-grained level. 4) robustness and natural application to real images. Our qualitative and quantitative analyses show RIS achieves both high-fidelity feature transfers and accurate fine-grained retrievals on real images. We discuss the responsible application of RIS.

本文提出了一种基于非监督框架的面部特征转移与检索方法，能够在真实图像上实现高保真的特征转移和精准的细颗粒度检索，突破了先前方法对超分辨率、姿态等限制，并探讨了其负责任的应用。

时尚检索：无监督人脸特征转移与检索