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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 Mercury 2?

Mercury 2 signifies a revolutionary leap in reasoning models, particularly tailored for instantaneous voice interactions, as it can promptly respond to incoming calls. In contrast to conventional autoregressive models that often leave callers waiting in silence while they generate responses sequentially, Mercury 2 uses a diffusion large language model architecture that can produce more than 1000 tokens per second on standard NVIDIA GPUs. This extraordinary processing speed enables it to finalize a complete reasoning cycle and start speaking in a timeframe that harmonizes with the natural flow of conversation, effectively reducing the usual wait time from several seconds to around 300 milliseconds. The functionality of Mercury models revolves around converting clear text into noise, after which a traditional Transformer is trained to reverse this process and predict the original text simultaneously across all positions. By adopting a denoising strategy that processes multiple tokens concurrently, the generation process becomes more efficient, achieving speeds comparable to customized silicon on NVIDIA H100s while enhancing responsiveness in voice applications. Consequently, Mercury 2 not only improves user interactions but also establishes a new benchmark for the field of interactive voice technology, paving the way for future advancements. With its innovative design, it promises to revolutionize the way users engage with voice systems.

Media

Media

Integrations Supported

Cerebras
GPT-4.1
Groq
Inception Labs
LiveKit
OpenAI
Pipecat
Retell AI
Vapi AI

Integrations Supported

Cerebras
GPT-4.1
Groq
Inception Labs
LiveKit
OpenAI
Pipecat
Retell AI
Vapi AI

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
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

Inception

Company Location

United States

Company Website

www.inceptionlabs.ai/blog/mercury-2-the-first-reasoning-model-fast-enough-to-pick-up-the-phone

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