# Phenaki

- Date: 2022-10-05
- Type: model
- Credit: Google Research
- Post: https://x.com/dumierhan/status/1577713150530445312
- URL: https://aifilmhistory.org/entry/phenaki

Google researchers, including Dumitru Erhan, introduce Phenaki, a text-to-video model that does something no other system could yet do: it takes a sequence of prompts that change over time and turns them into one continuous video, minutes long. Their showcase is a 2 minute 28 second story told through a chain of prompts, a first-person motorcycle ride that becomes a run through the woods, a backyard where an elephant and then a robot appear, and the robot dances a tango. Another chains a teddy bear swimming in the ocean at San Francisco, diving under the water and ending on a panda.

Under the hood, a new video tokenizer (C-ViViT) compresses footage into a small set of discrete tokens with causal attention in time, which is what lets clips run to any length, and a bidirectional masked transformer generates those tokens from text. Training jointly on a large set of image-text pairs and a smaller set of video-text pairs let it render things its video data never showed.

While other models of the day maxed out at a few seconds, Phenaki showed that long-form AI video was possible in principle, and that a story could be written as a timeline of prompts rather than a single one. Like Google's [Imagen Video](https://aifilmhistory.org/entry/imagen-video), announced the same week, it never left the lab, but the idea of a prompt sequence as a script came back in the storyboard and multi-shot tools that followed. Erhan went on to co-lead [Gemini Omni](https://aifilmhistory.org/entry/gemini-omni) at Google DeepMind.

Links:
- Project Page: https://phenaki.github.io/
- Paper: https://arxiv.org/abs/2210.02399
- Dumitru Erhan's announcement on X: https://x.com/dumierhan/status/1577713150530445312

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Source: https://aifilmhistory.org/entry/phenaki
Era: 2022 · The Dawn
Last updated: 2026-10-07