AI video looks wrong: how to fix hands, text, faces and physics

Most advice on this topic lists the faults. This page is about the fix: how to work out what caused a fault and which one thing to change before you spend more credits. A credit is the unit Higgsfield charges for each generation. If you want the review side, meaning a pass and fail checklist for client work, see the AI video quality check. It is not repeated here.

The method below is a working rule built from vendor and independent guides (Higgsfield, Morphic, Magic Hour and iTechGuides, listed in the sources). It is not a tested success rate, and none of those guides give one. Treat it as a way to stop guessing.

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Triage: find the first broken frame

Scrub through the clip and note the moment the fault first appears. Magic Hour's guide tells you to log defect timing, and iTechGuides says to change one variable per attempt. Put those together and the timing points to the likely cause:

The fault first appears	Likely cause	Change this one thing
In frame one, before anything moves	The starting image has the fault, and the video inherits it	Fix or replace the still image, then animate it
During one specific movement, such as a hand reaching or a head turning	The motion you asked for is too complex	Simplify the action to one clear move, or use slower pace words
Late in the clip, after a good start	The clip is too long and the model drifts	Shorten the clip, or make two short clips and join them
Throughout, the same way on every retry	The model or prompt is a poor match for the shot	Change the model, or change the approach (reframe, real footage)

This table is my synthesis, so adjust it to what you see. The useful part is the habit: read the timing before you touch the prompt.

The one-variable retry rule

Morphic's troubleshooting guide says that regenerating without changing anything just gives you a different version of the same problem. iTechGuides adds that repeated identical attempts do not show you what is causing the defect, and tells you to change one variable per attempt. I would go further and make it a rule for the team:

1. Write down the fault and where it first appears.
2. Pick one input from the table. Change only that.
3. Generate once. Compare.
4. If the fault moved or shrank, keep going the same direction. If it did not change, undo and try a different single input.

Changing five things at once may fix the clip, but you will not know why, and you will pay for new faults. Keep a one-line note per retry: what changed and what happened. After a week you will know which input fixes most of your faults, which is worth more than any list.

Hands and fingers

Extra or merged fingers are the most reported fault, and the cleanest fix is to avoid the problem. Frame the shot so hands are out of view, or hold something in a way that hides them. If hands must show:

• Give a reference. Higgsfield's own blog on hands and faces suggests a reference image of the hand position you want.
• Describe where things sit. Its prompt-mistakes article advises naming where every object is and how the hands make contact, and describing the start and end position instead of the motion.
• Keep the action small. One hand, one object, one movement.

Magic Hour adds that upscaling and face swap do not repair hand or body anatomy. Upscaling only sharpens what is there. If the fingers are wrong, make a new clip.

Higgsfield's blog also says, in a FAQ answer, that Seedance 2.0 handles hands most reliably when you add a reference image of the hand position. Seedance is a video model from ByteDance available inside Higgsfield. That claim comes from the vendor and comes with no comparison test. The help center's model guide, which is more careful, says Seedance is built for realistic motion, especially human movement, and says nothing about hands. So I would treat "Seedance handles hands best" as unproven and run your own side-by-side on your own shot.

Faces that drift or melt

If the face is wrong in frame one, the problem is the source image. If it is fine early and drifts late, shorten the clip. If it is wrong in every version, give the model a stronger anchor. Higgsfield recommends Soul ID, a saved face model you train from about 20 reference photos, for a consistent character. Use it only with photos of people who have agreed in writing. See the Soul ID guide and keeping a character the same in every post. Training cost is not published, so check the button before you start.

Other fixes that cost little:

• Start from a clean, well-lit, front-facing still with no sunglasses or hand across the face.
• Ask for less head movement.
• Use first and last frame control, where the model supports it, so the end of the clip is tied to a good frame. Higgsfield's model guide names Wan, a family of video models, for this.
• Make short clips and join them with cuts.

Garbled or changing text

Do not ask a video model to draw text you need to read. It draws shapes that look like letters, and the shapes often change from frame to frame. The reliable fix is to take text out of the model's job:

1. Leave the area clear. Prompt for a blank sign, a plain pack face or an empty space in the frame.
2. Add the real text in an editor. Use your editing software to place the words, price, phone number or logo from the approved file, so every letter is exact.
3. If you need text inside a still, use an image model built for it. Higgsfield's model guide points to GPT Image for text rendering. Make the still there, check it letter by letter, then animate it. See getting correct text onto images.

For legal copy such as prices, offers, addresses and phone numbers, never trust generated text, even if it looks right. Type it in the editor and check it against the client's approved sheet.

Physics and motion that feels fake

Liquids, cloth, falling objects and handoffs are where models struggle most. The fixes are about asking for less:

• One clear action per clip. "She lifts the cup" is better than "she lifts the cup, drinks, laughs and turns".
• Use pace words. Words such as "slow" and "steady" reduce frantic movement.
• Cut away before the hard moment. Show the pour starting, then cut to a face reacting, instead of showing the whole pour.
• Use first and last frames to anchor where the movement starts and ends.

If the shot depends on exact physics, such as a product being poured, a handoff or something spinning, plan to film it instead. See when not to use AI video.

Which Higgsfield model for which fault

This is where the platform helps. Higgsfield puts many models behind one account, so you can change the model without changing tools. Its help center's "which AI model should I use" article gives routing hints. Here they are, with what is and is not claimed:

Fault	Higgsfield suggestion	Caution
Text in a still	GPT Image	Still check every letter
Face that drifts	Soul ID	Needs about 20 reference photos and written consent
Human motion	Seedance, described as built for realistic human movement	Not claimed for hands
Control over start and end	Wan, first and last frame control	Check the options on screen
Hands	Nothing specific in the model guide	Reframe, reference image, or film it

Credit costs for most of these are not published, apart from a few listed rates, so check the cost on the Generate button and measure cost per approved clip, not cost per attempt. See how to spend Higgsfield credits wisely and starter prompts and templates.

One more thing that looks like a bad output: a blocked job. Higgsfield and its model providers can flag a prompt by mistake. A flagged generation returns no clip, does not appear in your assets, and credits are usually returned. Check Manage Account, then Usage, before you blame the prompt. If a job simply fails or hangs, see what to check when a generation fails or sticks.

Fix in post, regenerate or reshoot

The choice follows the fault. Brief version, because the full rule is on the quality check page:

• Fix in post for things that sit on top of the picture: text, logos, colour, a stray object.
• Regenerate with one changed input for anatomy, a drifting face or flicker.
• Reshoot when the shot needs hands holding the product, readable on-screen text, pouring liquid or a handoff, and your retry budget has run out.

Upscaling only helps softness. Higgsfield has upscale tools, covered in upscale, relight and edit, but they do not repair wrong anatomy.

Set a retry budget

Decide before you start how many single-change attempts a shot gets. A budget of four is a reasonable starting point for me, but it is a proposal, not a figure from any guide. Tally it in your notes:

1. Attempt 1: change the source image.
2. Attempt 2: simplify the action.
3. Attempt 3: shorten the clip.
4. Attempt 4: change the model.

If none of the four works, change the approach: reframe, use stock, or film it. iTechGuides cites a 2026 Runway study (1,043 viewers) in which detection accuracy was 58 to 65 percent for faces, hands and human action, against 45 to 47 percent for animals and architecture. I have only seen it secondhand, so treat the figures as reported. The sensible takeaway holds either way: spend review time on people shots, since viewers are most likely to notice errors there. For what customers notice, see how to tell if a video is AI-generated.

Where Higgsfield is a weak fit for this problem

If your shot needs exact hands, exact text or exact physics, no model setting guarantees it, and Higgsfield publishes no hands-specific guidance beyond its blog. Real footage or stock is the better tool then. Some other platforms offer partial clip editing, and Magic Hour cites Runway's edit tools for that, but that is unchecked, so test before you rely on them. Compare options in Higgsfield vs Runway and Higgsfield alternatives.

Who should skip Higgsfield for this: anyone who needs a product shot with readable packaging text in motion, or a hand-held demo that must be exact. Film it, or use a photo with a simple camera move.

How I'd approach this

I would keep a one-page fix card taped near the screen: first broken frame, one input to change, four attempts, then change the approach. I would add text in the editor every time, and keep hands out of frame unless the shot truly needs them. When a fault keeps repeating, I would stop retrying and ask whether the shot should be filmed.

If your team makes a lot of AI video, Blur Studio builds the repeatable side: a review step, a shared prompt library and a record of what fixed what. See the business page.