Movie101v2: Improved Movie Narration Benchmark
This work addresses the need for better movie narration to assist visually impaired audiences, but it is incremental as it builds upon existing datasets and methods.
The authors tackled the problem of automatic movie narration by introducing Movie101v2, a large-scale bilingual dataset with improved quality, and proposed a three-stage roadmap with evaluation metrics, resulting in baseline evaluations of models like GPT-4V that reveal significant challenges in generating applicable narrations.
Automatic movie narration aims to generate video-aligned plot descriptions to assist visually impaired audiences. Unlike standard video captioning, it involves not only describing key visual details but also inferring plots that unfold across multiple movie shots, presenting distinct and complex challenges. To advance this field, we introduce Movie101v2, a large-scale, bilingual dataset with enhanced data quality specifically designed for movie narration. Revisiting the task, we propose breaking down the ultimate goal of automatic movie narration into three progressive stages, offering a clear roadmap with corresponding evaluation metrics. Based on our new benchmark, we baseline a range of large vision-language models, including GPT-4V, and conduct an in-depth analysis of the challenges in narration generation. Our findings highlight that achieving applicable movie narration generation is a fascinating goal that requires significant research.