Core Viewpoint - The article discusses the advancements and challenges in personalized multi-person image generation using diffusion models, highlighting the innovative ID-Patch mechanism that addresses identity leakage and enhances accuracy in positioning and identity representation [1][5][21]. Group 1: Challenges in Multi-Person Image Generation - Personalized single-person image generation has achieved impressive visual effects, but generating images with multiple people introduces complexities [4]. - Identity leakage is a significant challenge, where features of different individuals can blend, making it difficult to distinguish between them [2][4]. - Existing methods like OMG and InstantFamily have attempted to tackle identity confusion but face limitations in efficiency and accuracy, especially as the number of individuals increases [4][14]. Group 2: ID-Patch Mechanism - ID-Patch is a novel solution designed specifically for multi-person image generation, focusing on binding identity and position [6][21]. - The mechanism separates facial information into two key modules, allowing for precise placement of individuals while maintaining their unique identities [9][21]. - ID-Patch integrates various spatial conditions, such as pose and depth maps, enhancing its adaptability to complex scenes [10][21]. Group 3: Performance and Efficiency - ID-Patch demonstrates superior performance in identity resemblance (0.751) and identity-position matching (0.958), showcasing its effectiveness in maintaining facial consistency and accurate placement [12]. - In terms of generation speed, ID-Patch is the fastest among existing methods, generating an 8-person group photo in approximately 10 seconds, compared to nearly 2 minutes for OMG [17][15]. - The performance of ID-Patch remains robust even as the number of faces increases, with only a slight decline in effectiveness [14][21]. Group 4: Future Directions - There is potential for further improvement in facial feature representation by incorporating diverse images of the same identity under varying lighting and expressions [20]. - Future explorations may include enhancing facial fidelity through multi-angle images and achieving dual control over position and expression using patch technology [22].