{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cvpr-2023-text-guided-video-editing","title":"CVPR 2023 Text Guided Video Editing Competition","arxiv_id":"2310.16003","date":"2023-10-24","proceeding":null,"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"],"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 https://sites.google.com/view/loveucvpr23/track4.","url_abs":"https://arxiv.org/abs/2310.16003v1","url_pdf":"https://arxiv.org/pdf/2310.16003v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"cvpr-2023-text-guided-video-editing","repo_url":"https://github.com/showlab/loveu-tgve-2023","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"video-editing","task_name":"Video Editing"},{"task_slug":"video-generation","task_name":"Video Generation"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2310.16003","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.16003"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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