Fix face and outfit drift in a football AI video

Find the frame where identity changes

Watch the clip once, then pause at the first frame where the scorer stops looking consistent. Note whether the change occurs at a turn, an occlusion, a cut or an abrupt scale change. These details are more useful than “the AI looks wrong.” Compare the same moment with the original photo. A head that is too small in every frame is a different problem from a convincing face that suddenly changes after a defender crosses it. Save the task ID alongside your notes so support can distinguish the result from a separate attempt.

Improve the identity evidence

Choose a sharper photo with an unobscured face, even lighting and a single adult subject. Remove ambiguity before adding prompt detail. A group shot with one person circled is still a picture of several people; a strong crop may be more useful. Do not rely on dark glasses, a hat brim or heavy skin smoothing to carry identity. Keep the original proportions and avoid stretching. If the clothing matters, use a photo that shows it rather than asking the model to invent a complete outfit from a face-only crop.

Reduce camera and scene changes

A rapid cut from a distant sideline view to a close-up asks the model to infer more identity detail than the first frame contained. Try a continuous wide or medium-wide shot with a stable direction of travel. Avoid dramatic spins that hide the face and then reveal a new angle unsupported by the photo. The simpler chip scene is a practical diagnostic because it uses fewer interactions than the complete goal. A successful simpler render still does not establish that a long multi-character sequence will remain consistent.

Make outfit instructions agree

Choose either to preserve the uploaded outfit or to request a deliberate change. Asking for the same pajamas and a red team kit at once creates a conflict. Fine patterns, small logos and moving text are particularly easy to distort. Describe the garment’s broad shape and color if you need extra direction. Watch sleeves, shoes and loose hems through the entire motion. A shirt that changes hue under lighting is not necessarily the same problem as a shirt that becomes a different garment at every cut.

Separate identity from football motion

A better identity photo will not reliably repair a broken ball trajectory. A cleaner motion clip will not recover facial information hidden by a scarf. Diagnose each separately. If a defender blocks the face at the moment of a change, try a reference with less occlusion or a wider spacing. If the full body changes, inspect whether the original image included enough of the body. Keep the scorer’s role explicit when the motion reference contains several players; the photo should define the intended replacement identity.

Decide whether to retry

A technical failure returns an error or failed task status. Identity drift in a completed video is a quality issue and should be evaluated under the published refund policy. Repeatedly pressing Generate creates separate requests and can spend credits again. Before another attempt, identify one specific change with a reasonable connection to the problem. Keep a useful result downloaded rather than relying on a temporary preview URL. When sharing, describe the clip as AI-generated and avoid implying the person actually performed the real-world football action.

Common questions

Take the next shot

Choose a prepared scene, use a clear authorized photo, and review the quote before generating. Inspect the complete result before sharing.