How to Fix Face Deformation and Inconsistency in AI-Generated Videos

In AI videos, when the same person “gets a different face in every shot,” it is usually not just bad luck. More often, the model is generating each shot independently and lacks a stable identity anchor. To fix facial deformation and inconsistency, the key is to lock the character identity first, then control the complexity of each shot, and finally use refinement as a fallback.

For creators who need a finished multi-shot video, you can use Image Fusion mode in NadouPro to establish the character identity, inject the same set of final-look references into each Video node on the canvas, and, when necessary, combine this with facial retouching and Portrait Enhancement. Below is a symptom-based checklist with an executable path.

What kind of “face collapse” are you seeing?

Compare the symptoms first, then choose an action. This avoids wasting time by rerunning blindly.

• Drifting across shots: Facial proportions and hairline edges change in every shot, looking “similar, but not like the same person.”
• Deformation after occlusion: After wearing a hat, passing through a doorway, or turning the head, the face becomes misaligned or loses features.
• Poor texture during motion: The face is roughly preserved, but the skin looks plastic, the eyes feel empty, and micro-expressions are stiff.
• Multiple people competing in close shots: When two people share a close-up frame, faces and hands are more likely to blur together.

Why does the same character become less and less recognizable?

Most generation pipelines only optimize whether “this segment looks good” each time. They do not automatically remember the face from the previous shot. Even very detailed text descriptions can correspond to countless valid faces; a single reference image can anchor one angle, but once the scene, lighting, or pose changes, that anchor can loosen easily.

Therefore, to stabilize a face, you need to do two things at the same time: give the model reusable identity memory, and reduce the number of changes that conflict with each other in a single generation.

Troubleshooting order from lowest to highest cost

1. Check the references and prompts first: Are the references clear, front-facing, visible, and free of sunglasses or masks? Does the prompt cram in too many actions and camera changes at once?
2. Then lock the identity: For any character who appears more than once, prioritize identity training with Image Fusion mode instead of temporarily attaching a new image for every shot.
3. Then adjust the generation method: Use the same character references across all Video node instances; split complex actions into separate shots; for multi-person close shots, generate people separately and composite them when possible.
4. Finally, repair after generation: If there are still subtle deviations, use facial retouching for small adjustments; if the skin is too smooth, use Portrait Enhancement.

How to stabilize faces in NadouPro

1. Prepare trainable character assets

Collect photos that are clear, evenly lit, and free of severe facial occlusion. It is recommended to cover the front view, three-quarter view, side view, and slight upward and downward angles. Quality matters more than quantity; Image Fusion training usually recommends more than twenty images. Keep the resolution ideally above 960 pixels, and avoid group photos and extreme expressions.

Stage output: a folder of character photos suitable for training, plus one main front-facing final-look image.

2. Use Image Fusion mode to establish identity instead of finding a face temporarily each time

Image Fusion mode learns facial structure and proportions from multiple photos to form a reusable identity. After that, calling the same identity across Video node instances on the canvas is more resistant to head turns and new lighting than “uploading one reference image for every shot.”

Checkpoint: Generate one still frame with different clothing and one with a different background. The facial proportions should still look like the same person. You can use a Multi-Grid or 3-view layout for a quick visual check.

3. Inject the same set of character anchors into each Video node

When creating a Video node for each shot on the canvas, put the same set of character final-look/trained identity references into the multi-reference input. The prompt should only describe this shot’s camera position, action rhythm, and environment. Do not reinvent a new face repeatedly.

Example input approach: the character reference is already locked; the prompt says “medium shot, body turns one quarter to the right, pauses, then raises eyes to look out the window, natural light enters from the left,” instead of a long description of facial features.

Checkpoint: Compare adjacent shots side by side to see whether the hairline, eye distance, and jawline remain continuous.

4. Handle each symptom with a separate branch

• Drifting across shots: Strengthen the diversity of Image Fusion training, reduce conflicting actions within a single shot, and keep the same reference set fixed.
• Face collapse after occlusion: Rely on identity locking during generation; if the face is still off after recovery, use facial retouching to adjust the mouth corners, eye position, and texture without rerendering the whole segment.
• Poor texture or empty eyes: First choose a model direction that is better suited for character performance, such as Kling 3.0 Omni or Seedance 2.5, then combine it with Portrait Enhancement.
• Multi-person close shots: Generating separately and then compositing is usually more stable than forcing everyone into one frame during generation.

5. Validate at low resolution first, then raise the specs

First validate composition, action, and facial consistency at a lower resolution. After confirming there are no issues, increase the resolution and add audio. High resolution can amplify flaws that are not visible at low resolution. In each iteration, try to change only one variable.

How do you know it is fixed?

Use at least three shots of the same character for a side-by-side check: a front-facing medium shot, a side view or head turn, and a recovery frame after occlusion. If the hairline, eye distance, nostril width, and jaw contour remain continuous, and the character still looks like the same person after changing outfits, the identity anchor is working. If only one shot looks good but the neighboring shot drifts, return to identity training and reference injection instead of only changing descriptive adjectives in the prompt.

Applicable limits

NadouPro can significantly improve face consistency across shots, but it cannot promise zero deformation under arbitrarily complex group scenes, extreme sports, or heavy occlusion. Identity changes, such as age spans, require separate references; outfit changes can usually be written in the prompt. Before commercial use, confirm the authorization for reference photos and follow the publishing platform’s labeling requirements for AI content.

Related questions

How many reference photos does Image Fusion mode need to stabilize a face?

At least twenty are recommended, with quality first: clear photos, visible faces, and coverage across angles and lighting. If the photos are blurry or look too similar to other faces, even a large quantity will not be easy to stabilize. After training is complete, the same identity can be reused in later multi-shot work.

If the face deforms after occlusion or a head turn, how can it still be fixed after generation?

First confirm that the Image Fusion identity or final-look reference was injected during generation. If there are still deviations, use facial retouching for local instruction-based adjustments, then use Portrait Enhancement to improve skin and sharpness. Subtle issues do not require rerunning the whole segment.

How do you keep a character’s face consistent across a multi-shot short film on the canvas?

Finish the character identity first, then place the same set of character references into every Video node, and connect the shots by splitting them into long shot, medium shot, and Close-Up. The prompt should only describe the blocking for the current shot; leave identity to the reference anchor, and accept the result by comparing adjacent shots side by side.

Summary and next step

Facial deformation and inconsistency are essentially caused by “no identity memory + too many changes in one generation.” First use Image Fusion to lock the character, then reuse the same references in canvas Video node instances, choose refinement or a more character-suited model based on the symptoms, and validate with a three-shot comparison at low resolution. You can start with one clear front-facing final-look image and run the smallest closed loop in NadouPro: identity training → two-shot validation → expanded shot breakdown.

Contains AI-generated information that may be incorrect. Please verify independently.