{"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/make-a-video-text-to-video-generation-without","title":"Make-A-Video: Text-to-Video Generation without Text-Video Data","arxiv_id":"2209.14792","date":"2022-09-29","proceeding":null,"authors":["Uriel Singer","Adam Polyak","Thomas Hayes","Xi Yin","Jie An","Songyang Zhang","Qiyuan Hu","Harry Yang","Oron Ashual","Oran Gafni","Devi Parikh","Sonal Gupta","Yaniv Taigman"],"abstract":"We propose Make-A-Video -- an approach for directly translating the tremendous recent progress in Text-to-Image (T2I) generation to Text-to-Video (T2V). Our intuition is simple: learn what the world looks like and how it is described from paired text-image data, and learn how the world moves from unsupervised video footage. Make-A-Video has three advantages: (1) it accelerates training of the T2V model (it does not need to learn visual and multimodal representations from scratch), (2) it does not require paired text-video data, and (3) the generated videos inherit the vastness (diversity in aesthetic, fantastical depictions, etc.) of today's image generation models. We design a simple yet effective way to build on T2I models with novel and effective spatial-temporal modules. First, we decompose the full temporal U-Net and attention tensors and approximate them in space and time. Second, we design a spatial temporal pipeline to generate high resolution and frame rate videos with a video decoder, interpolation model and two super resolution models that can enable various applications besides T2V. In all aspects, spatial and temporal resolution, faithfulness to text, and quality, Make-A-Video sets the new state-of-the-art in text-to-video generation, as determined by both qualitative and quantitative measures.","url_abs":"https://arxiv.org/abs/2209.14792v1","url_pdf":"https://arxiv.org/pdf/2209.14792v1.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":"make-a-video-text-to-video-generation-without","repo_url":"https://github.com/lucidrains/make-a-video-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"make-a-video-text-to-video-generation-without","repo_url":"https://github.com/xuduo35/MakeLongVideo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"super-resolution","task_name":"Super-Resolution"},{"task_slug":"text-to-video-generation","task_name":"Text-to-Video Generation"},{"task_slug":"video-generation","task_name":"Video Generation"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-to-video-generation-on-msr-vtt","task":"Text-to-Video Generation","dataset":"MSR-VTT","model":"Make-A-Video","rank_in_archive_order":12,"of":18,"metrics":{"CLIP-FID":"13.17","CLIPSIM":"0.3049","FID":"13.17"},"uses_additional_data":false},{"leaderboard":"/sota/text-to-video-generation-on-msr-vtt","task":"Text-to-Video Generation","dataset":"MSR-VTT","model":"CogVideo (English)","rank_in_archive_order":15,"of":18,"metrics":{"CLIP-FID":"23.59","CLIPSIM":"0.2631","FID":"23.59"},"uses_additional_data":false},{"leaderboard":"/sota/video-generation-on-ucf-101","task":"Video Generation","dataset":"UCF-101","model":"Make-A-Video (Finetuning, 256x256, class-conditional)","rank_in_archive_order":8,"of":48,"metrics":{"FVD16":"81.25","Inception Score":"82.55"},"uses_additional_data":false},{"leaderboard":"/sota/video-generation-on-ucf-101","task":"Video Generation","dataset":"UCF-101","model":"Make-A-Video (Zero-shot, 256x256, class-conditional)","rank_in_archive_order":31,"of":48,"metrics":{"FVD16":"367.23","Inception Score":"33"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2209.14792","atlas_url":"https://app.syntology.ai/?focus=2209.14792","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}