StableIdentity: Inserting Anybody into Anywhere at First Sight

Understand

Recent advances in large pretrained text-to-image models have shown unprecedented capabilities for high-quality human-centric generation, however, customizing face identity is still an intractable problem.

• Existing methods cannot ensure stable identity preservation and flexible editability, even with several images for each subject during training.
• In this work, we propose StableIdentity, which allows identity-consistent recontextualization with just one face image.
• More specifically, we employ a face encoder with an identity prior to encode the input face, and then land the face representation into a space with an editable prior, which is constructed from celeb names.