IC-Portrait: In-Context Matching for View-Consistent Personalized Portrait

Understand

Existing diffusion models show great potential for identity-preserving generation.

• However, personalized portrait generation remains challenging due to the diversity in user profiles, including variations in appearance and lighting conditions.
• To address these challenges, we propose IC-Portrait, a novel framework designed to accurately encode individual identities for personalized portrait generation.
• Our key insight is that pre-trained diffusion models are fast learners (e.g.,100 ~ 200 steps) for in-context dense correspondence matching, which motivates the two major designs of our IC-Portrait framework.