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，一种能够从多种条件信号合成高质量视频及匹配音频的语言模型。VideoPoet采用仅解码器架构的Transformer，可处理图像、视频、文本和音频等多模态输入。其训练流程遵循大型语言模型（LLMs）的范式，包含预训练和任务特定适应两个阶段。在预训练阶段，VideoPoet在自回归Transformer框架中融合了多模态生成目标的混合策略。预训练的LLM可作为基础模型，适应多种视频生成任务。本文通过实证结果展示了该模型在零样本视频生成中的最先进能力，特别强调了VideoPoet生成高保真运动效果的性能。项目页面：http://sites.research.google/videopoet/