Learning To Cut by Watching Movies

Alejandro Pardo, Fabian Caba, Juan Léon Alcázar, Ali K. Thabet, Bernard Ghanem; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6858-6868


Video content creation keeps growing at an incredible pace; yet, creating engaging stories remains challenging and requires non-trivial video editing expertise. Many video editing components are astonishingly hard to automate primarily due to the lack of raw video materials. This paper focuses on a new task for computational video editing, namely the task of raking cut plausibility. Our key idea is to leverage content that has already been edited to learn fine-grained audiovisual patterns that trigger cuts. To do this, we first collected a data source of more than 10K videos, from which we extract more than 260K cuts. We devise a model that learns to discriminate between real and artificial cuts via contrastive learning. We set up a new task and a set of baselines to benchmark video cut generation. We observe that our proposed model outperforms the baselines by large margins. To demonstrate our model in real-world applications, we conduct human studies in a collection of unedited videos. The results show that our model does a better job at cutting than random and alternative baselines.

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@InProceedings{Pardo_2021_ICCV, author = {Pardo, Alejandro and Caba, Fabian and Alc\'azar, Juan L\'eon and Thabet, Ali K. and Ghanem, Bernard}, title = {Learning To Cut by Watching Movies}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {6858-6868} }