Personalized image completion aims to restore occluded regions in personal photos while preserving identity and appearance. Existing methods either rely on generic inpainting models that often fail to maintain identity consistency, or assume that suitable reference images are explicitly provided. In practice, suitable references are often not explicitly provided, requiring the system to search for identity-consistent images within personal photo collections. We present AlbumFill, a training-free framework that retrieves identity-consistent references from personal albums for personalized completion. Given an occluded image and a personal album, a vision-language model infers missing semantic cues to guide composed image retrieval, and the retrieved references are used by reference-based completion models. To facilitate this task, we introduce a dataset containing 54K human-centric samples with associated album images. Experiments across multiple baselines demonstrate the difficulty of personalized completion and highlight the importance of identity-consistent reference retrieval. Project Page: https://liagm.github.io/AlbumFill/

翻译：个性化图像补全旨在修复个人照片中的遮挡区域，同时保持身份与外观一致性。现有方法要么依赖通用图像修复模型（常无法维持身份一致性），要么假设已明确提供合适的参考图像。但实际场景中，合适参考通常未经明确提供，系统需在个人相册中搜索身份一致的图像。我们提出AlbumFill——一种无需训练的框架，通过从个人相册中检索身份一致的参考图像实现个性化补全。给定遮挡图像与个人相册后，视觉语言模型推理缺失语义线索以引导组合式图像检索，检索到的参考图像被用于基于参考的补全模型。为支持该任务，我们构建了一个包含54K人类中心样本及其关联相册图像的数据集。跨多个基线的实验揭示了个性化补全的难度，并凸显了身份一致参考检索的重要性。项目主页：https://liagm.github.io/AlbumFill/