Results of the summarizing process for the arXiv paper: 2312.14125v1

VideoPoet is a language model that can generate high-quality videos with matching audio using various types of input signals. It utilizes a decoder-only transformer architecture to process multimodal inputs such as images, videos, text and audio. The model undergoes two stages of training: pretraining and task-specific adaptation. During pretraining, VideoPoet incorporates a mixture of generative objectives within an autoregressive Transformer framework. This pretrained model serves as a foundation for adapting to different video generation tasks and demonstrates its state-of-the-art capabilities in zero-shot video generation by producing temporally coherent videos with dynamic and meaningful motion. Despite only being able to view a short temporal context, VideoPoet can predict future frames while maintaining the consistency of object motion, style and identity across more than 8 seconds of video output. VideoPoet's training on videos, images and text enables it to understand various aspects of the world including 3D structures, camera motions and visual styles learned from different sources. Even without specific training data or losses for encouraging 3D consistency, the model can rotate around objects and visualize their backside accurately. Additionally, by using short text prompts VideoPoet can apply a range of camera motions to image-to-video and text-to-video generations. The model can also incorporate different visual styles such as watercolor or oil paintings into its generated videos; these stylization training sources primarily come from the text-image training data. The ability to combine these different styles highlights VideoPoet's understanding of objects in a temporal context. In conclusion, VideoPoet showcases the potential of large language models trained on discrete visual and audio tokens for generating high quality videos. Its strength lies in generating high fidelity motions in large and complex videos; the unified architecture and vocabulary used during training allow the pretrained model to excel at multi task video creation and serve as a foundation for various video related capabilities like editing.

• VideoPoet is a language model that generates high-quality videos with matching audio using different types of input signals.
• It utilizes a decoder-only transformer architecture to process multimodal inputs such as images, videos, text, and audio.
• The model undergoes two stages of training: pretraining and task-specific adaptation.
• During pretraining, VideoPoet incorporates generative objectives within an autoregressive Transformer framework.
• VideoPoet demonstrates state-of-the-art capabilities in zero-shot video generation by producing temporally coherent videos with dynamic and meaningful motion.
• Despite only seeing a short temporal context, VideoPoet can predict future frames while maintaining consistency in object motion, style, and identity across more than 8 seconds of video output.
• Training on videos, images, and text enables VideoPoet to understand various aspects of the world including 3D structures, camera motions, and visual styles learned from different sources.
• Without specific training data or losses for encouraging 3D consistency, VideoPoet can accurately rotate around objects and visualize their backside.
• Using short text prompts, VideoPoet can apply a range of camera motions and incorporate different visual styles into its generated videos.
• The ability to combine different styles highlights VideoPoet's understanding of objects in a temporal context.
• VideoPoet showcases the potential of large language models trained on discrete visual and audio tokens for generating high-quality videos.
• Its strength lies in generating high fidelity motions in large and complex videos.
• The unified architecture and vocabulary used during training allow the pretrained model to excel at multi-task video creation and serve as a foundation for various video-related capabilities like editing.

Introducing VideoPoet: A Language Model for Generating High Quality Videos

VideoPoet is a language model that can generate high-quality videos with matching audio using various types of input signals. It utilizes a decoder-only transformer architecture to process multimodal inputs such as images, videos, text and audio. The model undergoes two stages of training: pretraining and task-specific adaptation. This article will discuss the features and capabilities of VideoPoet in detail, providing an overview of its state-of-the-art performance in zero-shot video generation.

Pretraining

During pretraining, VideoPoet incorporates a mixture of generative objectives within an autoregressive Transformer framework. This pretrained model serves as a foundation for adapting to different video generation tasks and demonstrates its state-of-the-art capabilities in zero-shot video generation by producing temporally coherent videos with dynamic and meaningful motion. Despite only being able to view a short temporal context, VideoPoet can predict future frames while maintaining the consistency of object motion, style and identity across more than 8 seconds of video output.

Understanding 3D Structures & Camera Motions

VideoPoet's training on videos, images and text enables it to understand various aspects of the world including 3D structures, camera motions and visual styles learned from different sources. Even without specific training data or losses for encouraging 3D consistency, the model can rotate around objects and visualize their backside accurately. Additionally, by using short text prompts VideoPoet can apply a range of camera motions to image-to-video and text-to