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The infamous video of Will Smith eating spaghetti was not a real recording. It was an early text-to-video experiment posted to Reddit in late March 2023, most commonly associated with the ModelScope text-to-video system. The short clip shows a distorted, rapidly changing version of Smith attempting to eat spaghetti—and became an unforgettable demonstration of how poorly early AI video handled faces, hands, food, and continuous motion.
What the original video showed
At a glance, the scene is simple: a man resembling Will Smith sits at a table with spaghetti and tries to eat it. But almost every detail falls apart in motion. The face changes shape, the hands and arms deform, the fork and noodles shift unpredictably, and the relationship between the food, mouth, bowl, and table never remains stable.
The result is recognizable enough to communicate “Will Smith eating spaghetti,” but unstable enough to look disturbing. That combination made the clip both funny and unsettling. It was not authentic footage of Smith, a news report, or a sophisticated face-swap operation. It was primarily an experimental AI-generated video shared because its failures were so conspicuous.
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The post identified the prompt as “Will Smith eating spaghetti” and the main generation method as ModelScope text-to-video. The creator also described generating footage at 15 frames per second, converting it to 24 fps, using Flowframes to interpolate it to 48 fps, and applying slow motion. Those details describe that particular posted workflow; they were not a universal recipe required by ModelScope.
A contemporaneous report from Futurism helped bring the clip to a much wider audience.
What ModelScope text-to-video was
ModelScope text-to-video was one of the early publicly accessible systems that generated short video clips from written prompts. Its appearance coincided with a period when text-to-image tools were advancing rapidly, while video generation still struggled to preserve a subject and its surroundings from one frame to the next.
The Hugging Face discussion for the ModelScope demo preserves references to the exact Will Smith prompt. The Hugging Face ModelScope listings also provide historical context for the kind of public demonstrations through which early text-to-video systems were tested.
ModelScope did not “understand” the event in the human sense. It generated a sequence of images intended to match the prompt and resemble plausible movement. Producing a convincing still image of a person near a plate is much easier than maintaining the same person, fork, noodles, hands, mouth, and table across many frames while showing a physically coherent action.
Why the video looked so disturbing
Identity drift
The system could suggest Will Smith’s appearance, but it could not consistently preserve his face. Features moved or changed between frames, producing a face that seemed to morph into different versions of itself.
Anatomical instability
Hands, fingers, arms, facial features, and body contours were especially unreliable. Generative systems often struggle with parts that move, overlap, or appear in unusual positions. Eating places all of those difficulties in one scene.
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Broken food contact
Spaghetti is not a rigid object. It bends, overlaps, disappears behind other objects, and changes position as it is lifted. The model had to keep the noodles connected to the fork, the fork connected to the hand, and the food connected to the mouth. Instead, the noodles could merge with the face or behave as though they had no consistent physical location.
Temporal inconsistency
Real movement is continuous. A person lifts food, moves it toward the mouth, takes a bite, and chews. In the clip, movement can jump, reverse, or mutate from one moment to the next. Frame-rate conversion and interpolation may make motion smoother, but they do not repair an object that was already generated in the wrong place.
Uncanny motion and visual artifacts
The clip approximated the idea of eating without reliably representing the sequence of lifting, placing, chewing, and swallowing. Viewers also noticed watermark-like or stock-photo-style artifacts, which became part of the conversation around the source imagery and the system’s training-data influences.
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Nothing in the available evidence shows that the creator intended to make horror. The unsettling effect appears to have come mainly from technical failure: the system produced the broad concept while failing to model the underlying physical action.
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The documented evidence confirms the prompt, but not the creator’s reason for selecting Smith. A reasonable inference is that Smith is a highly recognizable public figure with extensive visual representation online. That makes the experiment easy to understand immediately—and makes identity failure easy to see.
Viewers could recognize the intended subject while watching that identity collapse from frame to frame. A less familiar face might have made the same technical problems harder to identify.
Did Will Smith really eat the spaghetti?
Not in the original clip. In February 2024, Smith posted a separate video parodying the meme. Reporting by MobileSyrup described the footage as a real video of Smith eating spaghetti, accompanied by the caption, “This is getting out of hand.” The post appeared on Smith’s official Instagram account.
That response is easy to confuse with the original because both depict Smith and spaghetti. They are different pieces of media:
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- The 2023 original: an AI-generated text-to-video experiment associated with ModelScope.
- The 2024 response: Smith’s real-life parody of the viral AI clip.
- Later recreations: new AI-generated versions made with newer systems and different workflows.
How it became an informal AI-video benchmark
By 2024, “Will Smith eating spaghetti” had become shorthand for a difficult video-generation test. Eating spaghetti combines several challenges that are obvious to human viewers:
- Keeping a recognizable identity stable.
- Rendering hands and fingers correctly.
- Maintaining the positions of the fork, bowl, noodles, face, and table.
- Depicting deformable food.
- Coordinating hand, head, mouth, and chewing movements.
- Showing believable contact, occlusion, and cause-and-effect motion.
- Synchronizing sound with the action when audio is included.
The phrase is sometimes called the “Will Smith Eating Spaghetti test” or even a video-model “unit test.” It is not an official benchmark. There is no fixed dataset, scoring system, required prompt, or pass/fail threshold. Users may compare different models with different prompts, durations, resolutions, settings, and editing processes.
For that reason, “passing” the test should always be attributed to a particular demonstration. A model may produce a more recognizable face or smoother movement than the 2023 clip while still failing to show food entering the mouth, preserving spaghetti structure, or synchronizing slurping sounds.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What improved in later recreations
Later demonstrations generally showed major gains in face stability, resolution, motion smoothness, and—in some cases—audio synchronization. The difference between the original clip and newer outputs illustrates how quickly generative video developed.
But community comparisons are demonstrations, not controlled scientific evaluations. Viewers have continued to identify problems including inaccurate facial appearance, spaghetti behaving like another material, weak or incorrect eating sounds, and food that never convincingly enters the mouth. Camera movement can also hide defects rather than prove that the model has maintained a stable scene.
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As of 2026, the meme continues to be reused as a quick qualitative comparison for new video systems. It remains useful precisely because people understand eating intuitively. They can tell when the action looks smooth but the physical story is wrong.
Why modern tools may not recreate it directly
Someone trying the original prompt today may receive a refusal, a generic actor, or a visibly inconsistent likeness. Hosted generators increasingly apply safeguards around living public figures, celebrity likenesses, copyrighted material, and deceptive media.
Tools such as Google Veo, OpenAI Sora, Runway, and Kling represent different contemporary approaches to video generation, but availability, access, moderation, regional support, pricing, and likeness policies can change. None should be assumed to permit a Will Smith prompt or to reproduce the original clip exactly.
For historical experimentation, the ModelScope demonstration is the relevant reference. A hosted community demo may be paused, rate-limited, or unreliable, and should not be treated as a supported production service.
What the spaghetti meme revealed
The clip became famous because it exposed a central weakness of early generative video: visual plausibility is not the same as physical understanding. The system could evoke a person, a table, a fork, and a meal, but it could not reliably preserve their identities and relationships over time.
That is why an ordinary action became such an effective stress test. People know how eating works. They understand where a fork should be, how noodles should move, and what the mouth should do. When any of those relationships breaks, the error is immediately visible.
The original Will Smith spaghetti video is therefore more than a bizarre internet joke. It is a compact record of an important moment in AI history—and a reminder that convincing individual frames do not guarantee convincing events.
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