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Link to original content: https://doi.org/10.48550/arXiv.2310.16003
[2310.16003] CVPR 2023 Text Guided Video Editing Competition
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Computer Science > Computer Vision and Pattern Recognition

arXiv:2310.16003 (cs)
[Submitted on 24 Oct 2023]

Title:CVPR 2023 Text Guided Video Editing Competition

Authors:Jay Zhangjie Wu, Xiuyu Li, Difei Gao, Zhen Dong, Jinbin Bai, Aishani Singh, Xiaoyu Xiang, Youzeng Li, Zuwei Huang, Yuanxi Sun, Rui He, Feng Hu, Junhua Hu, Hai Huang, Hanyu Zhu, Xu Cheng, Jie Tang, Mike Zheng Shou, Kurt Keutzer, Forrest Iandola
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Abstract:Humans watch more than a billion hours of video per day. Most of this video was edited manually, which is a tedious process. However, AI-enabled video-generation and video-editing is on the rise. Building on text-to-image models like Stable Diffusion and Imagen, generative AI has improved dramatically on video tasks. But it's hard to evaluate progress in these video tasks because there is no standard benchmark. So, we propose a new dataset for text-guided video editing (TGVE), and we run a competition at CVPR to evaluate models on our TGVE dataset. In this paper we present a retrospective on the competition and describe the winning method. The competition dataset is available at this https URL.
Comments: Project page: this https URL
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2310.16003 [cs.CV]
  (or arXiv:2310.16003v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2310.16003

Submission history

From: Jay Zhangjie Wu [view email]
[v1] Tue, 24 Oct 2023 16:56:58 UTC (24,717 KB)
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