Reelune

10 Motions That Break a 5-Second AI Video — Why We Banned Them and What We Use Instead

Spins, hops and hand-to-mouth gestures. Rejection rates, the actual ban list, and the lessons behind it

  • AI video glitches
  • AI video unnatural motion
  • image-to-video motion prompts
  • Wan 2.2 motion
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Generate short AI videos every day and a pattern emerges: some motions never produce a usable clip, no matter how many times you try. We put those motions on a permanent ban list. This article covers **10 of them: spins, hops and gestures around the mouth.** It is the production record — which motions, why each one went, what replaced it, and the rejection numbers that pushed us there.

The 10 banned motions, classified

CategoryExample we bannedWhat went wrongWhat we use instead
Full-body spins (4)Twirl on the spot with a flaring skirtToo fast for 5 s; the face leaves the camera and identity isn't heldSlow side-to-side sway / over-the-shoulder glance
Hops (1)Small hop with a quick half-spinToo fast; the face turns away before landingRising onto tiptoes, reaching upward
Fast choreography (1)Point, wave and shoulder bounce in quick rhythmLess than a second per action; reads as franticA single finger heart or peace sign
Hand to mouth (3)Surprise into laughter, hand near the mouthSurprise, laughter or embarrassment — it never resolvesA wave / peeking through both hands
Fingers on lips (1)Thoughtful finger to the lower lipThe gesture's meaning doesn't come acrossA sideways thinking glance that drifts back

The short version: 10 banned motions, two reasons

Banned motions stay defined in our library but can never be selected. This article covers 10 of them:

The reasons collapse into two groups:

  • Too fast for five seconds — the spins, the hop and the choreography (6)
  • The gesture doesn't read — viewers can't tell what it means (4)

The usual complaint about AI video is extra fingers and melting hands. Yet among the reasons we recorded for these 10 bans, not one is a hand or finger rendering glitch. What got motions banned was speed and ambiguity, not anatomy.

How these clips are made

You can't talk about motion failures without the pipeline, so here it is:

  1. Generate one start image with SDXL plus PuLID, which locks the character's facial identity
  2. Hand that image to Wan 2.2, an image-to-video model, along with a motion instruction written in English
  3. The model renders 81 frames at 16 fps — about 5.06 seconds
  4. A human reviews every clip before it is approved or rejected

The motion instruction is assembled from a library of predefined motions: one or two of them (usually two), joined with `, then ` into a single sentence, for example:

she lifts one hand and gives a small playful wave to camera, ... , then her gaze drifts off to the side as if thinking about something, ...

The key fact: the video model only ever sees that one start image. Anything not visible in it — including a face that turns away from the camera and comes back — has to be redrawn by the model mid-clip.

The data: more beats, more rejections

Our first suspect was simply how much motion we were cramming into five seconds. We counted the then separators in each clip's motion instruction — each one is a change of action, a "beat" — and compared that against how often human review rejected the clip.

Rejection rate rose steadily with every added beat.

The arithmetic explains why. Chain two motions into 5.06 seconds, give each an internal "spin → stop → pose" structure, and you get five beats in total — roughly one second per action. That's the speed at which a clip starts to look frantic.

Clips get rejected for many reasons, so this is a correlation, not proof of cause. The 4-beat bucket has only six samples and is indicative at best. But when the most common pattern at the time (2 beat changes, n=141) was rejected 62% of the time, overpacked motion was impossible to ignore.

Failure 1: spins, hops and fast choreography (6)

Here are the six we removed. Every one of them sounds charming on paper.

Banned motionWhat the instruction asked for
Shoulder roll into a turnSlow shoulder roll flowing into a turn, hair flaring, landing with a laugh
Twirl with a flaring skirtA light spin on the spot, the dress flares, she lands facing camera laughing
Hair flip plus spinA quick head turn to flip the hair, then a spin on the spot
Dress twirl, hem caughtTwirl in place, the hem flares at knee height, she catches it with one hand
Happy hop with a half-spinA small excited hop and a quick half-spin before landing
Snappy hand choreographyA point, a wave and a shoulder bounce, in quick rhythm

We recorded two reasons:

  1. Too fast for five seconds. As the numbers above show, each action gets about a second
  2. Spinning takes the face off camera, and the facial identity fixed in the start image can't be held once it comes back

The second reason follows directly from the pipeline: the model only has the start image, so the face that completes the turn is a face it drew from scratch.

To be clear, we did not ban dancing. Slow side-to-side sways, head-only turns toward the camera and over-the-shoulder glances are all still in use. What went was specifically: full-body spins, jumps and fast shaking.

Counting beats didn't catch the spins

If too many beats is the problem, cap the beats. So we set a hard limit: at most 3 beat changes per clip. Then we found something we hadn't expected:

  • 5 of the 6 banned motions contained no `then` at all
  • "Spin, hair flares, land facing camera, laugh" had been written as one comma-separated action

By the counter's logic, these were zero-beat, calm motions.

The cap actually changed only 1.0% of clips, and the average barely moved.

What worked was not the cap. It was identifying the fast motions by name and removing them, and rewriting instructions to drop repetition like "repeats the wave once more" so each motion is one clear action. The cap stays, but only as a safety net for extreme cases that slip through.

Failure 2: hand to mouth (3)

These three were removed for meaning, not speed.

Banned motionWhat the instruction asked for
Laugh, hand to mouthA sudden small laugh, a glance away with a hand to her mouth, then back to camera
Surprise turning to laughterEyes widen in surprise, then a real laugh, one hand near her mouth
Startled fingertips to lipsFingertips rise to the lips in a startled gesture and stay there

The verdict in review: you can't tell whether she's surprised or laughing.

In a still photo, a hand over a smiling mouth reads naturally. In five seconds of video, the moment the hand comes up could be surprise, laughter or embarrassment — and the clip ends before it resolves into any of them.

This is often misread, so: hands near the face are not banned. Motions still in use include:

  • Blowing a kiss — kept deliberately, because its intent is unambiguous and the ban's reason doesn't apply
  • Peace signs and finger hearts
  • Covering the face with both hands and peeking through the fingers
  • Resting a hand on the cheek with a head tilt

The test isn't "is the hand close to the face?" It's "after five seconds, can a viewer name what the gesture means?"

Failure 3: a thoughtful finger on the lip (1)

Two days after the hand-to-mouth ban, one more motion went for the same reason.

Banned motionWhat the instruction asked for
Thoughtful finger to lipFingers touch the lower lip for a beat, eyes drift aside, then return

The recorded reason matches the first group: in a five-second clip, a finger brought to the mouth becomes a picture of someone doing something unclear. A "thinking" pose that works in a photograph simply didn't read as thinking in motion.

A lesson in scope: the first ban targeted hands going to the mouth in surprise or laughter, so a thoughtful finger on the lip was left out. Different situation, same reason it fails. Define a ban by the reason you're banning, not by what the gesture looks like or when it happens, or the same failure survives in a different shape.

In its place we use a sideways glance that drifts back slowly, and a dreamy chin-on-hand pose.

Failures we fixed by rewriting, not banning

Some failures didn't need a motion removed — just a different way of writing the instruction. If you generate your own clips, these may be the most useful part of this page.

How it was writtenWhat happenedThe fix
Cheeks gradually flush mid-clipPink color blotchesNever describe color changing over time
Negations like "without smiling"The negation is lost; she smiles moreWrite the positive: "lips closed, calm expression"
A standing motion chained to a seated oneThe model tries to satisfy both and blends them into an unnatural poseKeep both chained motions in the same posture
Clothing moving with no hand moving itInvites fabric melting or passing through the bodyWrite the hand first, then have the hand move the garment
"Repeats it once more"The same action twice in five seconds, so it runs fastCollapse it into a single action

Posture conflicts turned up when we machine-generated 3,000 motion specifications as a test: 11.0% (330) contained incompatible postures. We fixed the selection step to align postures — and 6 (0.2%) still slipped through.

The cause: a later stage recombined the selected motions with results from a separate draw. Every part was correct; the assembly broke it.

Validate the final instruction that actually reaches the model, not the intermediate parts. We now also run an automated test on every selectable motion to confirm none contains a negation.

Not broken, still a failure: everyone is smiling

Separate from the ban list, a recount of the motion library turned up a different problem.

As of August 2026, 111 of 246 motions (45%) involved smiling or laughing. Among the general-purpose motions we use most, it was 33 of 37 (89%). Wistful expressions? Just 2.

No single clip had anything wrong with it. But lined up in a feed, every character was smiling the same smile. Individual videos fine; the site as a whole, monotonous.

The fix wasn't rewriting existing motions. It was adding options that don't smile:

  • One slow breath, shoulders settle, a calm steady look at the camera
  • A gaze that drifts aside as if thinking, then returns unhurried
  • Eyes closed for a beat as the air moves her hair
  • A small head tilt, as if listening to something off camera

We chose not to rewrite because it would have changed the look of clips already approved and published. When you want to shift a ratio, adding choices is far more predictable than editing the ones you have.

A checklist for your own 5-second AI clips

Here is everything above, condensed into a checklist for image-to-video generation. We verified it on 5-second Wan 2.2 clips, but the reasoning is about clip length and human viewers, so it should carry over to other short-form models.

  1. Three beat changes at most in five seconds — one or two is better
  2. Count comma-joined actions too. "Spins, stops, laughs" is three actions with no then in sight
  3. Avoid full-body spins, jumps and fast shaking. Turn the head or shoulders instead
  4. Keep the face in roughly the start image's orientation. A face that leaves the frame gets redrawn
  5. Bring hands to the face only for gestures with one clear meaning — a wave, a peace sign, hiding behind both hands. Skip hand-to-mouth and finger-to-lip
  6. Don't write "repeats" or "several times"
  7. Don't write negations. Describe the opposite state in positive terms
  8. Don't ask for color to change during the clip
  9. When chaining two motions, keep the posture the same
  10. Keep instructions short. We cut the median video-stage prompt from 799 to 576 characters

One last point: no amount of prompt hygiene replaces a human looking at every clip before it goes live. Every ban on this page came from a person watching a video and deciding it wasn't usable.

How we ban: disable, don't delete

None of the 10 banned motions in this article were deleted. Their definitions stay in the library; the selection step simply rejects them every time.

Three reasons for doing it this way:

  • Re-enabling is a one-line change. If a future model renders spins cleanly, we can re-test and restore
  • The reason for each ban sits right next to the motion in the code
  • When and why each ban happened stays traceable

Every number in this article was recounted from that record. The dates are there too: the three hand-to-mouth motions, the six spin/hop/choreography motions and the finger-on-lip motion were added on three consecutive days in late August 2026.

What we have not tested is whether spins work when described as slow. We chose to drop the fast motions and keep gentle sways instead, so we can't honestly claim that a spin is impossible however it's written.

FAQ

Why do spins so often break in AI videos?

We recorded two reasons. In a five-second clip a spin has very little time, so it looks too fast. And turning takes the face off camera, so the face that comes back can't be held to the start image's identity. This comes from reviewing real outputs and rejections; we haven't verified the model's internal mechanism.

Do hands near the face always glitch?

No. What we banned were gestures like hand-to-mouth and fingers-on-lips that don't resolve into one meaning within five seconds. Waves, peace signs, hiding behind both hands and blowing a kiss are all still in use. None of the 10 bans in this article were made because of finger or hand rendering errors.

How many actions fit in a 5-second AI video?

We cap it at three beat changes per clip. In our review data, rejection rates were 44% with zero changes, 52% with one and 62% with two. Beware that comma-joined actions look like zero beats to a simple counter, so count the actual actions by eye.

Couldn't you fix these with better prompts instead of banning them?

We tried capping the number of beats, and it affected only 1.0% of clips. We have not tested describing spins as slow; we chose to drop fast motions and keep gentle sways. The definitions were never deleted, so they can be re-tested and restored if models improve.

Can I prevent a motion by writing "don't do X"?

No. The negation gets lost — writing "without smiling" pushed our clips toward smiling. Describe the state you want in positive terms, such as "lips closed, calm expression". We run an automated check that no selectable motion contains a negation.

Does this apply to video models other than Wan 2.2?

We have only measured roughly five-second, image-to-video clips from Wan 2.2. That overpacked clips look rushed and ambiguous gestures don't read are about clip length and human viewers, so we expect them to carry over, but we haven't measured other models.