Nano Banana and Character Consistency in AI Videos

If you are trying to keep an AI-generated character consistent across video scenes, you already know the pain: one frame looks right, the next invents a new face. That break kills story immersion and brand trust.

This guide covers a practical workflow for character consistency in AI video, using a recurring hero like Nano Banana as the example. For broader video tooling, see how to make AI videos, Sora marketing use cases, and AI creative tools.

Why character consistency matters

Audiences track identity the same way they do in film. If hair, face shape, costume, or proportion drift every cut, the story feels unfinished. For brands, inconsistency also looks unprofessional and weakens recognition.

Consistency does not mean the character never changes emotion or angle. It means core identity stays locked while performance and camera move.

What usually breaks consistency

• Prompts that re-describe the character differently each time
• Lighting and camera changes without reference control
• Model randomness when seeds and references are not locked
• Switching tools mid-sequence without a shared reference pack

Build a character bible first

Before generating motion, define a one-page character sheet:

1. Master portrait: a clear front-facing reference image
2. Costume rules: colors, materials, logos, silhouette
3. Face traits: age range, eye shape, hair, distinguishing marks
4. Do-not-change list: the traits that must survive every scene

Reuse that language in every prompt. Do not invent a new description from memory each time.

A practical consistency workflow

1. Lock the reference. Generate or choose one master still of the character.
2. Generate still variants. Use the same reference for angle and expression tests before any video.
3. Storyboard beats. Decide scene count, camera moves, and emotional arc on paper.
4. Generate clips from the same identity pack. Prefer image-to-video or reference-conditioned tools over pure text-to-video for identity-critical shots.
5. Edit and review. Cut weak frames, re-roll only the broken shots, keep winners.

Tools that help (and how to use them)

• Runway: strong for motion drafts once you have a solid still reference
• Stable Diffusion + ControlNet: useful for locking pose and composition in stills before animation
• Pika: fast short motion tests when you need quick iterations
• Google Veo / Sora-class models: higher realism passes after identity is already stable

Pick one primary pipeline. Switching models every scene is a common cause of face drift.

Prompt and seed habits that work

• Reuse the same character descriptor block in every prompt
• Keep seed values when a look is working
• Change only action and camera language between shots
• Add negative prompts for "different face, new person, identity change"

Brand and production checklist

• Does the character match the master portrait at a glance?
• Are costume colors and logos intact?
• Would a viewer recognize them after a 2-second cutaway?
• Is any claim or brand mark accurate and approved?

If you are shipping this for a brand campaign, WorkWithAI can help through AI creatives and branding workflows that keep identity and message aligned.

FAQ

Can pure text-to-video keep a character consistent?
Sometimes for a few shots, but reference-based workflows are more reliable for series and brand work.

How many reference images do I need?
Start with one excellent master portrait, then add 2-3 approved angle variants once the look is locked.

Should I regenerate the whole scene when one frame drifts?
Usually no. Re-roll the broken shot with the same reference pack and keep the good clips.

Is Nano Banana a real product?
Treat it as a character example for this workflow. The same consistency method applies to any recurring AI hero or brand mascot.

Next steps

Build the character bible, lock one master still, then produce a 3-scene test before a longer film. Consistency is a process, not a single prompt.