Retrieve In Style: Unsupervised Facial Feature Transfer And Retrieval

Abstract

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 ap

Stats

• citations20
• S2 citations—
• github stars0
• HF likes0
• heat score9.92
• arxiv keychong2021retrieve

Related papers

• RASR: Retrieval-augmented Super Resolution For Practical Reference-based Image Restoration (2025)0.00
• Towards Unsupervised Sketch-based Image Retrieval (2021)0.00
• Stylemeup: Towards Style-agnostic Sketch-based Image Retrieval (2021)14.69
• Arnet: Self-supervised FG-SBIR With Unified Sample Feature Alignment And Multi-scale Token Recycling (2024)5.84
• Learnt Quasi-transitive Similarity For Retrieval From Large Collections Of Faces (2016)5.24
• Reuse Your Features: Unifying Retrieval And Feature-metric Alignment (2022)1.69
• Risa-net: Rotation-invariant Structure-aware Network For Fine-grained 3D Shape Retrieval (2020)5.48
• From A Glance To "gotcha": Interactive Facial Image Retrieval With Progressive Relevance Feedback (2020)0.00