  This work studies how to transform an album to vivid and coherent stories, a
task we refer to as "album storytelling". While this task can help preserve
memories and facilitate experience sharing, it remains an underexplored area in
current literature. With recent advances in Large Language Models (LLMs), it is
now possible to generate lengthy, coherent text, opening up the opportunity to
develop an AI assistant for album storytelling. One natural approach is to use
caption models to describe each photo in the album, and then use LLMs to
summarize and rewrite the generated captions into an engaging story. However,
we find this often results in stories containing hallucinated information that
contradicts the images, as each generated caption ("story-agnostic") is not
always about the description related to the whole story or miss some necessary
information. To address these limitations, we propose a new iterative album
storytelling pipeline. Specifically, we start with an initial story and build a
story-aware caption model to refine the captions using the whole story as
guidance. The polished captions are then fed into the LLMs to generate a new
refined story. This process is repeated iteratively until the story contains
minimal factual errors while maintaining coherence. To evaluate our proposed
pipeline, we introduce a new dataset of image collections from vlogs and a set
of systematic evaluation metrics. Our results demonstrate that our method
effectively generates more accurate and engaging stories for albums, with
enhanced coherence and vividness.
