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What is VideoPoet?

VideoPoet is a groundbreaking modeling approach that enables any autoregressive language model or large language model (LLM) to function as a powerful video generator. This technique consists of several simple components. An autoregressive language model is trained to understand various modalities—including video, image, audio, and text—allowing it to predict the next video or audio token in a given sequence. The training structure for the LLM includes diverse multimodal generative learning objectives, which encompass tasks like text-to-video, text-to-image, image-to-video, video frame continuation, inpainting and outpainting of videos, video stylization, and video-to-audio conversion. Moreover, these tasks can be integrated to improve the model's zero-shot capabilities. This clear and effective methodology illustrates that language models can not only generate but also edit videos while maintaining impressive temporal coherence, highlighting their potential for sophisticated multimedia applications. Consequently, VideoPoet paves the way for a plethora of new opportunities in creative expression and automated content development, expanding the boundaries of how we produce and interact with digital media.

What is Marengo?

Marengo is a cutting-edge multimodal model specifically engineered to transform various forms of media—such as video, audio, images, and text—into unified embeddings, thereby enabling flexible "any-to-any" functionalities for searching, retrieving, classifying, and analyzing vast collections of video and multimedia content. By integrating visual frames that encompass both spatial and temporal dimensions with audio elements like speech, background noise, and music, as well as textual components including subtitles and metadata, Marengo develops an all-encompassing, multidimensional representation of each media piece. Its advanced embedding architecture empowers Marengo to tackle a wide array of complex tasks, including different types of searches (like text-to-video and video-to-audio), semantic content exploration, anomaly detection, hybrid searching, clustering, and similarity-based recommendations. Recent updates have further refined the model by introducing multi-vector embeddings that effectively separate appearance, motion, and audio/text features, resulting in significant advancements in accuracy and contextual comprehension, especially for complex or prolonged content. This ongoing development not only enhances the overall user experience but also expands the model’s applicability across various multimedia sectors, paving the way for more innovative uses in the future. As a result, the versatility and effectiveness of Marengo position it as a valuable asset in the rapidly evolving landscape of multimedia technology.

Media

Media

Integrations Supported

TwelveLabs

Integrations Supported

TwelveLabs

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

$0.042 per minute
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Google

Company Website

sites.research.google/videopoet/

Company Facts

Organization Name

TwelveLabs

Date Founded

2021

Company Location

United States

Company Website

www.twelvelabs.io/product/models-overview#marengo

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