Text-to-video generation task has witnessed a notable progress, with the generated outcomes reflecting the text prompts with high fidelity and impressive visual qualities. However, current text-to-video generation models are invariably focused on conveying the visual elements of a single scene, and have so far been indifferent to another important potential of the medium, namely a storytelling. In this paper, we examine text-to-video generation from a storytelling perspective, which has been hardly investigated, and make empirical remarks that spotlight the limitations of current text-to-video generation scheme. We also propose an evaluation framework for storytelling aspects of videos, and discuss the potential future directions.
@article{arxiv.2405.08720,
title = {The Lost Melody: Empirical Observations on Text-to-Video Generation From A Storytelling Perspective},
author = {Andrew Shin and Yusuke Mori and Kunitake Kaneko},
journal= {arXiv preprint arXiv:2405.08720},
year = {2024}
}
Comments
To appear at CVPR 2024 Workshop on AI for Content Creation (AI4CC)