Transcript to Video: Efficient Clip Sequencing from Texts
The Chinese University of Hong Kong1      Adobe Research2     


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. We will release codes and models.

Demo Video



    author = {Xiong, Yu and Caba, Fabian and Lin, Dahua},
    title = {Transcript to Video: Efficient Clip Sequencing from Texts},
    journal={arXiv preprint arXiv:TBD},
    month = {July},
    year = {2021}


Xiong Yu(熊宇): xy017 [AT]