Meta unveils Movie Gen, its model for creating video and sound

Meta unveiled Movie Gen this Friday, a family of artificial intelligence models capable of creating videos with synchronized sound from a written description. The system can also modify existing videos: changing objects, transforming settings or altering specific elements without manually editing the footage frame by frame.

The announcement matters because it brings together two capabilities that have so far usually arrived separately: moving-image generation and audio generation. Rather than producing a silent video and then requiring another tool to add music, effects or ambient sound, Movie Gen aims to build both layers in coordination.

Up to 16 seconds of high-definition video

The family’s main component is Movie Gen Video, a model with 30 billion parameters. Parameters are the internal values a model adjusts during training to learn patterns in images, text and sound. Meta says it can generate clips of up to 16 seconds at 1080p resolution and a frame rate of 16 frames per second.

The company showcases text-generated scenes featuring people, animals and complex environments, as well as sequences in which characters perform actions described in the prompt. As with other video generators, the challenge is not just producing an attractive image: it is maintaining subjects’ identities, object consistency and plausible physics throughout the sequence.

Movie Gen is entering a race that already includes OpenAI with Sora, Runway with Gen-3 Alpha, Luma AI and Chinese companies such as Kling. Video generation has become one of the most visible fronts in generative AI because it requires combining language understanding, visual composition, motion and temporal continuity.

Sound is no longer an external add-on

Meta has also developed Movie Gen Audio, a model with 13 billion parameters designed to generate soundtracks, effects and ambient sounds from text, video or both. It can, for example, pair the sound of an engine with a moving vehicle, create a musical backing track for a scene or add ambient sounds that fit the setting shown.

The key detail is synchronization. In conventional video, sound is usually added during post-production and requires human decisions: when music starts, which effect should accompany an action or how it should be mixed with the ambient sound. Meta’s proposal attempts to automate the relationship between what happens on screen and what viewers hear.

Even so, generating plausible sound is not the same as fully solving dubbing or character dialogue. Human speech requires lip-sync, performance, continuity across shots and control over language—all areas that remain particularly challenging for generative tools.

Edit a video with a single sentence

Movie Gen does not only work from a blank screen. Meta has shown features for editing existing footage through written instructions. The system can add an object to a scene, replace the background, change a person’s appearance or modify specific elements without manually reconstructing every shot.

It also includes a video personalization feature: given an image of a person, the model can place them in a generated scene while preserving their features. This capability is appealing for personalized content, advertising and audiovisual creativity, but it raises familiar concerns around consent and impersonation. The easier it becomes to produce a realistic sequence featuring someone’s likeness, the more important it will be for platforms and creators to clearly indicate when an image has been generated or altered by AI.

A research advance, not an available application

Meta has not made Movie Gen generally available to the public. The company presents Movie Gen as research and has not announced its availability as a public product.

That caution has a practical reason. Generated video could be used to preview campaigns, create educational material, prototype scenes or produce social media content with fewer resources. But the same techniques lower the cost of making deceptive videos, imitating real people or reusing works and styles without authorization.

The decisive comparison will not be limited to the quality of demonstration clips. Movie Gen will have to show that it offers enough control for real-world production: consistency across scenes, precise editing, rights to the materials used and effective mechanisms for identifying synthetic content. Meta has shown ambitious technology; what remains to be seen is under what conditions it decides to turn it into an accessible tool.