AlbumFill: Album-Guided Reasoning and Retrieval for Personalized Image Completion

Problem
This paper addresses the gap in personalized image completion, specifically the challenge of restoring occluded regions in personal photos while maintaining identity and appearance consistency. Existing methods either utilize generic inpainting models that struggle with identity preservation or require explicit reference images, which are often unavailable in practice. The authors propose a novel approach, AlbumFill, which operates without the need for pre-provided references, instead retrieving identity-consistent images from personal photo collections. This work is presented as a preprint and has not yet undergone peer review.

Method
AlbumFill is a training-free framework that leverages a vision-language model to infer semantic cues from an occluded image and its associated personal album. The core technical contribution lies in the retrieval mechanism, which identifies suitable reference images that maintain identity consistency for the completion task. The authors introduce a new dataset comprising 54,000 human-centric samples paired with album images to facilitate the evaluation of their method. The framework employs reference-based completion models that utilize the retrieved images to fill in the occluded regions effectively. The architecture details, loss functions, and specific training compute requirements are not disclosed, as the method is designed to be training-free.

Results
The authors conduct experiments against multiple baselines to demonstrate the efficacy of AlbumFill in personalized image completion. They report significant improvements in completion quality, particularly in maintaining identity consistency, compared to traditional inpainting methods. While specific numerical results are not provided in the abstract, the authors emphasize the challenges posed by personalized completion tasks and the critical role of identity-consistent reference retrieval. The results indicate that AlbumFill outperforms existing methods, highlighting the importance of context-aware retrieval in enhancing completion outcomes.

Limitations
The authors acknowledge several limitations in their work. First, the reliance on personal albums means that the effectiveness of AlbumFill is contingent on the availability and quality of the album images. Additionally, the framework may struggle with highly diverse or low-quality personal photo collections, which could hinder the retrieval of suitable references. The authors do not discuss potential biases in the dataset or the implications of using a vision-language model, which may affect generalizability across different demographics or image types.

Why it matters
The implications of this work are significant for downstream applications in personalized image editing and restoration. By enabling systems to autonomously retrieve identity-consistent references from personal collections, AlbumFill enhances the user experience in photo editing tools and could lead to advancements in automated content generation. This approach also opens avenues for further research into context-aware retrieval mechanisms and their integration with generative models, potentially influencing future developments in personalized AI-driven image processing.

Authors: Yu-Ju Tsai, Brian Price, Qing Liu, Luis Figueroa, Daniil Pakhomov, Zhihong Ding, Scott Cohen, Ming-Hsuan Yang
Source: arXiv:2605.02892
URL: https://arxiv.org/abs/2605.02892v1