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/

翻译：我们提出VideoPoet，这是一种能够根据多种条件信号合成高质量视频并匹配音频的语言模型。VideoPoet采用仅解码器架构的Transformer，可处理多模态输入——包括图像、视频、文本和音频。其训练流程遵循大语言模型（LLM）的标准范式，包含预训练和任务特定适配两个阶段。在预训练阶段，VideoPoet在自回归Transformer框架内融合了多模态生成目标的混合训练。预训练后的LLM作为基础模型，可适配多种视频生成任务。我们通过实证结果展示了该模型在零样本视频生成中的先进性能，特别强调了VideoPoet生成高保真运动的能力。项目主页：http://sites.research.google/videopoet/