Google’s Lumiere: New AI Model that Creates Realistic Videos

Google’s Lumiere is a cutting edge text-to-video model that uses a new technique to create realistic videos from short text inputs.

Google researchers came up with a new AI model that can generate realistic videos from short text inputs.
Lumiere works differently from existing video generation models, focusing on the movement of objects in the image.
It generates more frames per video than Stable Video Diffusion and can reimagine content from short prompts.

Google has revealed an innovative text-to-video model called Lumiere, capable of generating realistic videos based on short text inputs. Unlike existing video generation models, Lumiere, as outlined in the paper titled ‘A Space-Time Diffusion Model for Video Generation,’ creates videos with lifelike motion by generating the temporal duration of the entire video at once. This differs from other models that synthesize distant keyframes followed by temporal super-resolution.

In essence, Lumiere focuses on the movement of objects in the image, in contrast to previous systems that piece together a video from keyframes where the movement has already occurred. The model excels in generating videos with 80 frames, providing smoother motion compared to competitors like Stability’s Stable Video Diffusion, which produces 14 to 25 frames.

Google's Lumiere
Google's Lumiere: New AI Model that Creates Realistic Videos 5

Lumiere has demonstrated superior performance in comparison to rival video generation models from Pika, Meta, and Runway across various tests, including zero-shot trials, according to Google’s team. The researchers argue that Lumiere’s unique approach results in state-of-the-art generation outputs, making it suitable for content creation tasks and video editing. This includes applications such as video inpainting and stylized generation, where it can mimic artistic styles presented to it by leveraging fine-tuned text-to-image model weights.

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Credit: Google

To attain its outcomes, Lumiere utilizes a novel architecture known as Space-Time U-Net. This architecture enables the generation of the complete temporal duration of a video in a single pass through the model.

According to the Google team, this innovative approach enhances the consistency of outputs. The team explained, “By employing both spatial and, notably, temporal down- and up-sampling and utilizing a pre-trained text-to-image diffusion model, our model is trained to directly produce a full-framerate, low-resolution video by processing it across multiple space-time scales.”

Google's Lumiere

Credit: Google

The Lumiere project aimed to develop a system that empowers inexperienced users in the creation of video content more effortlessly.

Nevertheless, the paper acknowledges the potential risk of misuse, specifically cautioning that models like Lumiere could be employed to generate deceptive or harmful content. The paper emphasizes the importance of creating and implementing tools for detecting biases and malicious applications to guarantee a safe and equitable use of the technology.

As of the current writing, Google has not released the model to the public. However, you can explore several example generations on the showcase page available on GitHub.

Related: Unlocking Creativity: A Guide to Getting Started with Gemini Pro API on Google AI Studio

Google steps up video work

Lumiere is the successor to VideoPoet, a multimodal model created by Google that generates videos from text, video, and image inputs. Introduced in December of the previous year, VideoPoet utilizes a decoder-only transformer architecture, enabling it to produce content it hasn’t been explicitly trained on.

Google has developed various video generation models, including Phenaki and Imagen Video. Additionally, the company has plans to utilize its detection tool SynthID to cover AI-generated videos.

Google’s efforts in the realm of video align with its Gemini foundation model, particularly the Pro Vision multimodal endpoint. This endpoint is capable of handling both images and videos as input, generating text as an output.

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