Google VideoPoet LLM for text-to-video , image-to-video , video stylization, video-to-audio
Science & Technology
Introduction
Google researchers have developed an impressive large language model (LLM) for zero-shot video generation called VideoPoet. This innovative model allows for various types of media input, such as text and images, to generate highly detailed and stylized videos as output. In this article, we will delve into the various features and capabilities of VideoPoet, including its applications in text-to-video generation, image-to-video transformation, video editing, stylization, inpainting, and more.
How Does VideoPoet Work?
Text-to-Video
The core functionality of VideoPoet allows users to input a text prompt to generate a video. For example, a prompt like "a dog listening to music with headphones in highly detailed 8K" would result in a corresponding video output.
Image-to-Video
VideoPoet also supports the generation of videos from still images, guided by additional text prompts. For instance, an image of a geyser could be transformed into a video showing the geyser spraying water with added motion and context from the prompt.
Video Editing
Users can edit existing videos by changing text prompts over time to produce visual stories. For example, inputting a prompt to transform a walking figure into a figure made of water, with lightning flashes and purple smoke, results in a coherent video as specified.
Stylization
The model allows for video stylization where an input video can be altered based on a guided text prompt. For instance, transforming a plain geyser video into a stylized version with pink and blue confetti and candy-coated trees based on the given prompt.
Inpainting
VideoPoet also supports inpainting, which can add details to masked-out portions of a video. For example, adding a pink teddy bear riding a toy train in the specified masked area.
Detailed Model Explanation
Overview
VideoPoet is an advanced LLM that can handle multiple types of inputs, including text, images, depth, optical flow, masks, and videos. Unlike previous text-to-video models based on latent diffusion techniques, VideoPoet utilizes LLMs to operate on discrete tokens.
Tokenization and Encoding
- Visual Tokens: Created by the MAGC-ViT-V2 encoder for video input.
- Audio Tokens: Generated by the SoundStream encoder for audio input.
- Text Tokens: Handled by the text encoder to produce tokens. These tokens are then processed by the VideoPoet LLM, which is an autoregressive model, to produce output tokens. The outputs are subsequently transformed back into their original formats using decoders corresponding to their respective encoders.
Use Cases and Applications
- Long Video Generation: Allows for the creation of extended video sequences by predicting successive one-second clips.
- Image to Video Control: Applies motion to static images to edit their content dynamically based on text prompts.
- Camera Motion Effects: Includes zoom-out, Dolly zoom, pan, and more for rich video effects.
Conclusion
While more details about the training process are not yet disclosed, VideoPoet demonstrates extensive capabilities in multimedia generation and editing. There is a potential for this model to be released as an API on the Google Cloud platform. For further technical details, a linked research paper and an official website are available for in-depth exploration.