IC-Portrait: In-Context Matching for View-Consistent Personalized Portrait
It addresses the problem of generating consistent and high-fidelity personalized portraits for users, though it appears incremental as it builds on existing diffusion models with novel adaptations.
The paper tackled the challenge of personalized portrait generation by proposing IC-Portrait, a framework that uses in-context matching to enhance identity preservation and view consistency, resulting in significant improvements over state-of-the-art methods in both quantitative and qualitative evaluations.
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. Specifically, we reformulate portrait generation into two sub-tasks: 1) Lighting-Aware Stitching: we find that masking a high proportion of the input image, e.g., 80%, yields a highly effective self-supervisory representation learning of reference image lighting. 2) View-Consistent Adaptation: we leverage a synthetic view-consistent profile dataset to learn the in-context correspondence. The reference profile can then be warped into arbitrary poses for strong spatial-aligned view conditioning. Coupling these two designs by simply concatenating latents to form ControlNet-like supervision and modeling, enables us to significantly enhance the identity preservation fidelity and stability. Extensive evaluations demonstrate that IC-Portrait consistently outperforms existing state-of-the-art methods both quantitatively and qualitatively, with particularly notable improvements in visual qualities. Furthermore, IC-Portrait even demonstrates 3D-aware relighting capabilities.