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How AI video generators work
Video generation extends image generation across time. A model has to produce visual content and coordinate how it changes from frame to frame. In OpenAI’s February 2024 technical report, Sora’s process is described in several stages:
- Compress the video. Sora turns raw video into a lower-dimensional representation, called a latent representation, that is smaller than the original pixel data and captures information across space and time.
- Divide it into spacetime patches. The compressed representation is split into patches that serve as tokens for a transformer—the units the model processes when generating a sequence.
- Generate and refine the patches. The model receives noisy patches along with conditioning, such as a text prompt, and learns to predict cleaner versions.
- Decode the result. A decoder maps the generated latent representation back into pixels to produce a viewable video.
One way to picture the process is as progressively refining a rough, noisy draft of a moving scene. That is an explanatory analogy, not a literal account of every internal step. OpenAI’s December 2024 system card likewise describes Sora as starting with a video resembling static noise and gradually removing noise over many steps. It says the model can look ahead across frames, which helps it preserve a subject when that subject briefly disappears from view.
These details describe Sora, not all AI video generators. Other tools may use different architectures, and OpenAI’s public report does not disclose every model or implementation detail.
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Prompts are one kind of conditioning
A prompt guides the kind of sequence the model generates; it does not specify or guarantee every pixel and movement. Sora’s December 2024 system card describes several input and editing modes: generating video from text, animating a still image, extending an existing video, and filling missing frames. These modes change what the system starts from, but each still requires it to generate visual information across time.
Why AI-generated video can look unrealistic
Consistency is harder than making one good frame
A single frame can look convincing while the sequence as a whole does not. A person’s face or clothing may shift; an object may change shape or disappear; a background, light source, or spatial relationship may drift between frames. OpenAI’s 2024 technical report calls maintaining temporal consistency in long videos “a significant challenge.” It says Sora can model short- and long-range dependencies often, but not always, including an object’s persistence when it is occluded.
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Those examples describe things a viewer can look for, not defects whose frequency has been measured by the cited sources. The report does not provide a general error rate, and its claims about Sora should not be treated as measurements of every generator.
Motion must fit the scene
It is not enough for a generated person or object to look plausible in isolation. Their movement should make sense in relation to other objects, surfaces, and the camera. A hand might appear to pass through an object, contact might look weightless, or a moving camera might reveal inconsistent scene geometry. These are useful checks when judging coherence, not a documented list of failure rates.
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OpenAI’s report discusses video models as possible “world simulators,” but that is a research framing, not proof that Sora—or any other generator—has a general-purpose physics engine or verified physical understanding. A clip can have persuasive composition and still fail to maintain coherent events.
Compression is context, not a proven cause of a specific flaw
Sora’s documented pipeline compresses video into a latent representation and later decodes it back into pixels. It is reasonable to note that compression and reconstruction involve representing and rebuilding visual information, but the cited descriptions do not establish those stages as the cause of any particular artifact. A specific flicker or change in a clip should not be attributed to compression without supporting evidence.
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How to judge whether a generated video feels realistic
Realism is more than sharpness or an attractive opening frame. When reviewing a clip, check the sequence across time:
- Frame appearance: Do individual frames look coherent, or are there visible distortions?
- Subject and object identity: Do people, clothing, and objects retain their appearance as the clip progresses?
- Motion and contact: Do movements and interactions look plausible, including when objects touch or move together?
- Object permanence: Does an object remain consistent when another subject or the camera briefly hides it?
- Scene geometry: Do backgrounds, distances, and spatial relationships remain stable as the camera moves?
- Cause and effect: Do events follow one another coherently, rather than merely looking plausible as isolated moments?
- Prompt adherence: Does the video depict the requested subjects and action throughout, not only in its first moments?
This is a practical viewing guide, not a standardized benchmark. A comparison between tools would also need to account for each tool version’s supported duration and aspect ratios, as well as its editing and control options; a current cross-provider ranking is not established by the sources cited here.
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What Sora’s published limits do—and do not—tell you
OpenAI published different capability figures in separate documents in 2024. They describe different contexts and should not be merged into one current limit:
| OpenAI publication | What it stated | How to interpret it |
|---|---|---|
| February 15, 2024 technical report | Research Sora could generate up to one minute of high-fidelity video. | A description of research capability in that report, not a verified product limit for October 2026. |
| December 9, 2024 system card | The product was described as supporting up to 1080p resolution and a maximum duration of 20 seconds. | A dated system-card statement, not confirmation of the product’s current limits. |
The December 2024 card also says Sora accepts text, image, and video inputs. Its description of training data mentions selected public data, proprietary partnership data, and human data; it does not fully enumerate datasets, and those disclosures should not be generalized to other providers.
Realistic-looking video is not proof of authenticity
A video’s visual quality does not establish whether it was recorded or generated. OpenAI’s December 2024 system card describes provenance measures the company pursued, including metadata, watermarks, and fingerprinting. That documents an intent to support provenance; it does not validate a universal detector or show that every generated video can be reliably identified. When the origin matters, check the source and any available provenance information rather than treating realism—or an apparent artifact—as proof.
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