Veo 3, Google’s new video-generating model that’s been making waves across the internet, is now available for everyone in public preview, the company announced Thursday.
The tool was initially available only to subscribers of Gemini Ultra and through Flow, Google’s AI-powered filmmaking platform that was also revealed at the most recent I/O. As of Thursday, it can be accessed as a public preview by all Google Cloud customers and partners in the Vertex AI Media Studio.
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Unveiled last month at I/O, Google’s annual developer conference, Veo 3 is able to generate video with synchronized audio — a long-standing technical challenge in the field. Imagine you prompt the system to generate a video set inside a busy subway car, for example. Veo 3 can produce the video, complete with AI-generated ambient background noise to add to the sense of realism. You can even prompt it to generate audio of human voices, according to Google.
The model also specializes in realistically simulating real-world physics, such as the fluid dynamics of water and the movement of shadows, making it a potentially valuable tool for filmmakers and advancing Google’s broader mission of bringing usable AI to creative industries.
Users can create videos on Veo 3 via natural language text prompts, fine-tuning their instructions to modify subtle creative details — “from the shade of the sky to the precise way the sun hits the water in the afternoon light,” the company wrote in a blog post on Thursday.
Use cases – and drawbacks
Google noted in its blog post that a range of companies are actively experimenting with Veo 3 to generate customer-facing content, including social media ads and product demos, as well as internal materials like training videos. One CEO described it as “the single greatest leap forward in practically useful AI for advertising since gen AI first broke into the mainstream in 2023.”
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Google and other leading AI developers have been investing heavily in tools designed to generate video from natural language prompts, betting that this will be a major practical use case for generative AI. AI avatar company Synthesia, for example, offers the technology as a way to make enterprise content faster and with fewer resources, including by letting users, like CEOs, replicate their likeness to create company video addresses.
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The response among creative professionals has been mixed. Some see positive potential for the future of AI-assisted filmmaking; the acclaimed director Darren Aronofsky, for one, has formed a creative partnership with Google DeepMind. A similar deal has been struck between Lionsgate and the AI start-up Runway.
Others, however, have been critical of the growing encroachment of AI-generated video throughout creative industries. A video ad for Toys R’ Us created using OpenAI’s Sora last year, for example, received widespread online ridicule. Unions of entertainment workers are organizing to protect their jobs as the technology rapidly evolves.
That hasn’t stopped tech companies from building and releasing new video-generating tools for marketers. Earlier this month, Amazon Ads announced the general launch across the US of its Video Generation tool; Meta has set its sights even higher, reportedly aiming to automate every step of the ad production process.
A major technical challenge
Veo 3 represents one of the first models from a major tech developer that can synchronize AI-generated video and audio. Meta’s Movie Gen, released in October, is another. Some other tools, like Runway’s Gen-3 Alpha, come with features that enable AI-generated audio to video in a post-production process, but the concurrent generation of the two requires the compute and resources of a major force like Google.
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Building AI models capable of generating synchronized video and audio has been a thorny technical challenge and an active area of research across the AI industry. Both AI-generated video and AI-generated audio are distinct technical challenges, and fusing them introduces a whole new dimension of complexity. Here’s a demo of Veo 3.
For one thing, video is a series of still frames, whereas audio is a continuous wave. Syncing the two therefore requires models that can operate across these two modalities, accounting for the vastly different timescales in which they operate.
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An AI model fusing video with sound must also be able to dynamically account for variables like material, distance, and speed. A car driving at 100 miles per hour sounds a lot different than one traveling at 10 miles per hour; a horse walking on cobblestones sounds different than one that’s walking on grass.