  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 https://sites.google.com/view/loveucvpr23/track4.
