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Humans watch more than a billion hours of video per day. Most of this videowas edited manually, which is a tedious process. However, AI-enabledvideo-generation and video-editing is on the rise. Building on text-to-imagemodels like Stable Diffusion and Imagen, generative AI has improveddramatically on video tasks. But it's hard to evaluate progress in these videotasks because there is no standard benchmark. So, we propose a new dataset fortext-guided video editing (TGVE), and we run a competition at CVPR to evaluatemodels on our TGVE dataset. In this paper we present a retrospective on thecompetition and describe the winning method. The competition dataset isavailable at https://sites.google.com/view/loveucvpr23/track4.

CVPR 2023 Text Guided Video Editing Competition