MAJORFailure Reference

Vidu Face Distortion — Pre-Generation Risk Reference

Technical Classification

Facial Identity Coherence Failure

Facial Identity Coherence Failure on Vidu occurs when the model fails to maintain the same facial identity across the clip's temporal axis. The face slowly drifts toward a different person, features (eyes, nose, mouth) misalign frame-to-frame, or the entire face structure collapses during expression changes. Vidu's Reference-to-Video mode is meant to prevent this — but on prompts with multiple subjects or rapid head turns, the reference identity gets averaged with hallucinated alternatives.

How to identify this failure

✕Subject's face becomes a different person by mid-clip
✕Eyes misaligned, nose shifts position frame-to-frame
✕Reference-to-Video identity drifts despite a clean reference image
✕Face structure collapses during smile or speech
✕Multiple subjects swap faces between cuts

Real generation examples

Prompt used

"Portrait of a woman smiling, soft window light, cinematic"

Failure observed @ 2.7s

Face structure changed at 2.7s — eye spacing widened, nose flattened, became a different person

Prompt used

"Two friends laughing at a coffee shop, handheld feel"

Failure observed @ 3.4s

Face swap occurred between subjects at 3.4s, both identities lost reference fidelity

Documentation strength

If you need to escalate

HIGH — Vidu support recognizes face-identity drift as a documented failure mode, especially for paid Reference-to-Video credits.

AVA is a pre-purchase prevention tool, not a post-purchase recovery tool. Platforms generally do not guarantee credit refunds for output-quality failures; goodwill credits are at each platform's discretion. The strength rating reflects how well-formed your support ticket can be, not a promised outcome.

Prevention + documentation steps

01
Score your prompt before you generate
Run your prompt through AVA's pre-flight scoring against the Facial Identity Coherence Failure pattern. Green light = generate. Yellow/red = rewrite using the suggested fix before you commit credits.
02
Capture Generation ID + timestamp if it failed anyway
Find the Generation ID in the URL or share link. Note the exact time when the Facial Identity Coherence Failure first appears (e.g. "failure first visible at 1.2s"). Timestamped evidence is significantly stronger than a general complaint.
03
Use the correct technical term in your support ticket
Describe this failure as "Facial Identity Coherence Failure". This term maps to a recognised internal workflow in the support system and routes the ticket to the right team.
04
Submit via the correct support channel
Runway has no direct email intake. Pro+ plan: open the in-app AI Assistant (help widget bottom-right of app.runwayml.com), describe the failure with the technical term, attach evidence. Free/Standard plan: human support isn't available — your channel is Discord #community-help with @On Call - Moderators.

Frequently asked questions

Why does Vidu's face drift even with a reference image?

The Reference-to-Video pipeline conditions on the reference, but the temporal coherence module can drift when the subject turns >45° or speaks. The reference signal weakens as the clip progresses.

Which Vidu prompts are highest risk for face distortion?

Long clips (>5s) with talking-head shots, profile turns, multi-subject scenes, and rapid expression changes. AVA pre-flight flags these.

Does Vidu refund face drift on Reference-to-Video?

Yes — and Reference-to-Video has stronger escalation precedent because the user is explicitly paying for identity preservation. Document with the Generation ID and reference-vs-output comparison.

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