ID-Patch: Robust ID Association for Group Photo Personalization

TL;DR

ID-Patch introduces a new method for robust identity association in group photo synthesis, improving face resemblance, positional accuracy, and efficiency while reducing ID leakage compared to existing techniques.

Contribution

The paper presents ID-Patch, a novel approach that generates ID patches and embeddings from facial features to enhance personalized group photo synthesis.

Findings

Outperforms baseline methods in face ID resemblance.

Achieves higher ID-position association accuracy.

Demonstrates improved generation efficiency.

Abstract

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/