Real-time generative video is video produced by an AI model continuously, live, such that input can change the output while it's being generated — as opposed to standard generative video, which is rendered once, offline, and delivered as a finished clip.
Generative video, broadly, means video created by an AI model rather than filmed with a camera. Most generative video products work in a single pass: you submit a prompt, the system renders a clip over a period of seconds to minutes, and you download or view the finished file. Real-time generative video removes that wait entirely. Generation and viewing happen at the same moment, frame by frame, so a live input — a camera feed, a gesture, a changed prompt — can influence what the model produces next, before the "clip" is ever finished, because there effectively is no fixed endpoint while the session continues.
This distinction matters because it changes the relationship between a person and the video. With offline generative video, a person is a requester who waits for a result. With real-time generative video, a person is closer to a participant — the video keeps evolving in response to what they do, similar to a live video call rather than a rendered export.
Real-time generative video is easiest to understand by taking the phrase apart. Generative means the frames are synthesized by an AI model, not captured through a lens or cut from existing footage. Video means a continuous stream of frames rather than a single still image. Real-time is the load-bearing word: the model keeps pace with the moment, producing each new frame fast enough that you perceive the result as it happens instead of waiting for a file to finish.
Put those together and the defining property is steerability during generation. In an offline pipeline the prompt is locked before rendering starts — you cannot change your mind halfway through a frame you haven't seen yet. In a real-time system there is no clean "before" and "after," because the generation is ongoing. Every new signal you send — turning your head, raising a hand, swapping a reference image, rewriting a prompt — folds into the very next frames. The output is a moving target you shape as it plays.
That is what separates real-time generative video from both filmed video and conventional AI clips. Filmed video is real light hitting a sensor. Conventional AI video is synthetic but frozen the instant it finishes rendering. Real-time generative video is synthetic and still in motion — closer to a conversation than a download.
Real-time generative video depends on a model fast enough to produce a usable frame within a small fraction of a second, so the delay between input and output is imperceptible. That typically involves:
The result behaves less like watching a video and more like a live, evolving feed that happens to be entirely AI-generated.
For most of the current generative-video era, "AI video" has meant batch rendering: describe a scene, wait, receive a clip. That approach is powerful for finished, cinematic output, but it keeps the human on the outside of the loop. Real-time generative video moves the human inside the loop, which unlocks a different class of uses — live performance, interactive filters, virtual presence on a video call, playable AI scenes — that a render-and-wait tool simply cannot serve.
The direction of travel is toward lower latency and longer, more coherent sessions. Newer systems are increasingly described as interactive and steerable rather than one-shot, and consumer products are pushing the experience into the browser so that no specialized hardware is required. As models get faster and cheaper to run per frame, the line between watching AI video and using AI video live keeps thinning. See /glossary/interactive-world-model for the systems designed specifically around this kind of live responsiveness, and /glossary/real-time-video-diffusion for one of the model techniques that makes low-latency frame generation feasible.
Real-time generative video shows up wherever the output has to react, not just play back:
These uses differ wildly in purpose, but they share the defining trait: the video is being generated at the same moment you are influencing it.
LiveGen is a consumer application of real-time generative video, built on the Xmax X2.0 model. Open livegen.ai in a browser, allow camera access, and the platform begins generating a transformed version of your live video immediately — no download, no render queue, no app to install. Modes like Face Swap, Style Morph, and Freestyle all run on this same real-time generative foundation, so the output updates continuously as you move or adjust your prompt, and can be shared the moment you're happy with it. Because everything runs in the cloud and streams back to a standard <video> element, you don't need a powerful GPU — a phone or laptop with a camera is enough.
A typical AI video clip is generated once from a prompt and delivered as a finished file. Real-time generative video is produced continuously and changes based on ongoing input, with no separate wait or export step before you can see it.
No. Most consumer platforms, including LiveGen, run the generation in the cloud and stream the result to your browser, so a standard device with a camera is enough.
No. A livestream transmits real footage instantly. Real-time generative video is generated by an AI model in real time — the visual content itself is synthetic, not captured.
Yes. Platforms built for this category, including LiveGen, typically let you capture and share a clip from your live session once you're satisfied with the result.
They're part of the same broader wave. Both are described as real-time, interactive AI video systems, though built for different purposes — see /glossary/genie-3 and /glossary/pixverse-r1 for details on each.
Not exactly. Real-time video diffusion is a model technique — a way of running a diffusion model fast enough to output frames live. Real-time generative video is the broader experience category, which can be produced by diffusion or by other fast generation methods. See /glossary/real-time-video-diffusion.
No. Preset modes like Face Swap or Style Morph work from a reference image and your live camera, so you can transform the feed without writing anything. A prompt is optional for open-ended modes like Freestyle when you want full control.
Yes. Because browser-based platforms do the heavy generation in the cloud and stream frames back, the experience works on a modern phone or tablet, not just a high-end desktop.
Open your camera and become anyone — free to start, no sign-up for your first try.
Start generating free