Transcript to Video: Efficient Clip Sequencing from Texts

• URL: http://arxiv.org/abs/2107.11851v2
• Date: Mon, 20 Nov 2023 02:14:36 GMT
• Title: Transcript to Video: Efficient Clip Sequencing from Texts
• Authors: Yu Xiong, Fabian Caba Heilbron, Dahua Lin
• Abstract summary: We present Transcript-to-Video -- a weakly-supervised framework that uses texts as input to automatically create video sequences from an extensive collection of shots. Specifically, we propose a Content Retrieval Module and a Temporal Coherent Module to learn visual-language representations and model shot sequencing styles. For fast inference, we introduce an efficient search strategy for real-time video clip sequencing.
• Score: 65.87890762420922
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Among numerous videos shared on the web, well-edited ones always attract more attention. However, it is difficult for inexperienced users to make well-edited videos because it requires professional expertise and immense manual labor. To meet the demands for non-experts, we present Transcript-to-Video -- a weakly-supervised framework that uses texts as input to automatically create video sequences from an extensive collection of shots. Specifically, we propose a Content Retrieval Module and a Temporal Coherent Module to learn visual-language representations and model shot sequencing styles, respectively. For fast inference, we introduce an efficient search strategy for real-time video clip sequencing. Quantitative results and user studies demonstrate empirically that the proposed learning framework can retrieve content-relevant shots while creating plausible video sequences in terms of style. Besides, the run-time performance analysis shows that our framework can support real-world applications.

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