{"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/videopoet-a-large-language-model-for-zero","title":"VideoPoet: A Large Language Model for Zero-Shot Video Generation","arxiv_id":"2312.14125","date":"2023-12-21","proceeding":null,"authors":["Dan Kondratyuk","Lijun Yu","Xiuye Gu","José Lezama","Jonathan Huang","Grant Schindler","Rachel Hornung","Vighnesh Birodkar","Jimmy Yan","Ming-Chang Chiu","Krishna Somandepalli","Hassan Akbari","Yair Alon","Yong Cheng","Josh Dillon","Agrim Gupta","Meera Hahn","Anja Hauth","David Hendon","Alonso Martinez","David Minnen","Mikhail Sirotenko","Kihyuk Sohn","Xuan Yang","Hartwig Adam","Ming-Hsuan Yang","Irfan Essa","Huisheng Wang","David A. Ross","Bryan Seybold","Lu Jiang"],"abstract":"We present VideoPoet, a language model capable of synthesizing high-quality video, with matching audio, from a large variety of conditioning signals. VideoPoet employs a decoder-only transformer architecture that processes multimodal inputs -- including images, videos, text, and audio. The training protocol follows that of Large Language Models (LLMs), consisting of two stages: pretraining and task-specific adaptation. During pretraining, VideoPoet incorporates a mixture of multimodal generative objectives within an autoregressive Transformer framework. The pretrained LLM serves as a foundation that can be adapted for a range of video generation tasks. We present empirical results demonstrating the model's state-of-the-art capabilities in zero-shot video generation, specifically highlighting VideoPoet's ability to generate high-fidelity motions. Project page: http://sites.research.google/videopoet/","url_abs":"https://arxiv.org/abs/2312.14125v4","url_pdf":"https://arxiv.org/pdf/2312.14125v4.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":[],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"large-language-model","task_name":"Large Language Model"},{"task_slug":"text-to-video-generation","task_name":"Text-to-Video Generation"},{"task_slug":"video-generation","task_name":"Video Generation"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-to-video-generation-on-msr-vtt","task":"Text-to-Video Generation","dataset":"MSR-VTT","model":"VideoPoet","rank_in_archive_order":4,"of":18,"metrics":{"CLIPSIM":"0.3123","FVD":"213"},"uses_additional_data":false},{"leaderboard":"/sota/text-to-video-generation-on-ucf-101","task":"Text-to-Video Generation","dataset":"UCF-101","model":"VideoPoet","rank_in_archive_order":6,"of":10,"metrics":{"FVD16":"355"},"uses_additional_data":false},{"leaderboard":"/sota/video-generation-on-ucf-101","task":"Video Generation","dataset":"UCF-101","model":"VideoPoet (text-conditional)","rank_in_archive_order":29,"of":48,"metrics":{"FVD16":"355","Inception Score":"38.44"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2312.14125","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}