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We present VideoPoet, a language model capable of synthesizing high-qualityvideo, with matching audio, from a large variety of conditioning signals.VideoPoet employs a decoder-only transformer architecture that processesmultimodal inputs -- including images, videos, text, and audio. The trainingprotocol follows that of Large Language Models (LLMs), consisting of twostages: pretraining and task-specific adaptation. During pretraining, VideoPoetincorporates a mixture of multimodal generative objectives within anautoregressive Transformer framework. The pretrained LLM serves as a foundationthat can be adapted for a range of video generation tasks. We present empiricalresults demonstrating the model's state-of-the-art capabilities in zero-shotvideo generation, specifically highlighting VideoPoet's ability to generatehigh-fidelity motions. Project page: http://sites.research.google/videopoet/

VideoPoet: A Large Language Model for Zero-Shot Video Generation