We present VideoPoet, a language model capable of synthesizing high-quality video 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 the ability to generate high-fidelity motions.