ID-Patch: Robust ID Association for Group Photo Personalization
This addresses identity leakage issues in group photo personalization for creative applications, representing an incremental improvement over existing methods.
The paper tackles the problem of identity leakage in personalized group photo synthesis, where facial features interfere, and proposes ID-Patch to robustly associate identities with 2D positions, resulting in improved face resemblance, accuracy, and efficiency compared to baselines.
The ability to synthesize personalized group photos and specify the positions of each identity offers immense creative potential. While such imagery can be visually appealing, it presents significant challenges for existing technologies. A persistent issue is identity (ID) leakage, where injected facial features interfere with one another, resulting in low face resemblance, incorrect positioning, and visual artifacts. Existing methods suffer from limitations such as the reliance on segmentation models, increased runtime, or a high probability of ID leakage. To address these challenges, we propose ID-Patch, a novel method that provides robust association between identities and 2D positions. Our approach generates an ID patch and ID embeddings from the same facial features: the ID patch is positioned on the conditional image for precise spatial control, while the ID embeddings integrate with text embeddings to ensure high resemblance. Experimental results demonstrate that ID-Patch surpasses baseline methods across metrics, such as face ID resemblance, ID-position association accuracy, and generation efficiency. Project Page is: https://byteaigc.github.io/ID-Patch/