MovieTeller: Tool-augmented Movie Synopsis with ID Consistent Progressive Abstraction explores MovieTeller provides a tool-augmented framework for generating accurate and coherent movie synopses using existing VLMs and face recognition tools.. Commercial viability score: 7/10 in Video Summarization.
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The automated generation of movie synopses caters to the increasing demand for digital content organization, efficient media archiving, and personalized recommendations. Without tools like MovieTeller, managing extensive video libraries is labor-intensive and inconsistent.
Productize as a SaaS tool for streaming platforms, offering easy integration for automated synopsis generation, improving user interaction with content via better indexing and searchability by maintaining narrative coherence and factual accuracy.
MovieTeller has the potential to replace manual synopsis generation processes typically handled by human editors, offering faster turnaround and consistency across content libraries.
Video content storage and streaming platforms require efficient content management solutions. With millions of titles and upwards of 10,000 minutes to catalog, there's a need and willingness to pay for automated solutions that can systematically generate factual and engaging synopses.
A platform offering automated synopses generation for streaming services to improve search and discovery, enhance user recommendations, and assist in content archiving.
The paper introduces MovieTeller, a tool-augmented progressive abstraction framework for generating synopses. It uses existing vision-language models enhanced with a face recognition tool to maintain character consistency and narrative coherence without retraining. The system extracts keyframes, uses face recognition to establish identity, and progressively abstracts narratives through multi-stage summarization.
The MovieTeller framework was tested on an extensive dataset of over 100 full-length movies, demonstrating state-of-the-art improvements in character consistency and narrative coherence over baseline models. Evaluations included both automatic and human assessments, showing significant preference and performance gains.
The primary limitation is reliance on the quality of external face recognition tools, which may misfire in low-quality or occluded frames. Moreover, processing very large volumes could be computationally expensive initially.
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