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

• URL: http://arxiv.org/abs/2501.17159v2
• Date: Fri, 31 Jan 2025 11:36:27 GMT
• Title: IC-Portrait: In-Context Matching for View-Consistent Personalized Portrait
• Authors: Han Yang, Enis Simsar, Sotiris Anagnostidis, Yanlong Zang, Thomas Hofmann, Ziwei Liu,
• Abstract summary: IC-Portrait is 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 for in-context dense correspondence matching. We show that IC-Portrait consistently outperforms existing state-of-the-art methods both quantitatively and qualitatively.
• Score: 51.18967854258571
• License:
• Abstract: 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.

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