Input: character reference sheet and shot list
Output: three recognizable shots from one character
Aspect: keep one ratio across shots
Build log
1. Define non-changeable traits
Face, hair, clothing, one accessory, skin tone, and light direction. I do not let the model invent a second accessory.
2. Make a shot set
I test close-up, medium action, and end frame. This reveals whether identity survives camera distance.
3. Retry in order
First shorten motion, then simplify background, then tighten the reference crop. Only after that change models.
4. Save the character set
I tag the reference sheet in the asset library so the next episode starts from the same person.
Complete execution record
Task boundary and source gate
Treat Consistent character AI video generator as a deliverable, not a definition. The job receives character reference sheet and shot list and owes three recognizable shots from one character in keep one ratio across shots. Write the rejection reason first, then turn it into the first hard constraint.
Require a character sheet, prop state, location notes, color grade, time of day, sound intention, and shot order. Do not let a strong style erase story time, character state, or the audience's understanding of who did what.
The imgmov route
Treat Canvas as the continuity ledger. Each node stores reference, prompt, camera, duration, and dependency. Generate the missing bridge only after stills and story beats are approved.
Keep platform requirements outside the model prompt. The prompt can describe a scene; a named checklist records the crop, claim, consent, and console check that make it deliverable.
Lock the variables that cannot drift
Lock face, costume, props, grade, lens height, location, story time, and dialogue line. Continuity beats novelty.
Put the locked fields at the top of the prompt, not at the end: Test three shots before writing a full script. A 5-second Agnes probe costs 10 credits, so identity problems surface before a Seedance 480p render at 90 credits or Kling at 195 credits. Save that prompt with the reference, ratio, and model so the next run starts from a decision instead of a guess.
Step-by-step build path
1) Define non-changeable traits: Face, hair, clothing, one accessory, skin tone, and light direction. I do not let the model invent a second accessory. Leave one checkable artifact from this step; do not start the next until it exists. 2) Make a shot set: I test close-up, medium action, and end frame. This reveals whether identity survives camera distance. Leave one checkable artifact from this step; do not start the next until it exists. 3) Retry in order: First shorten motion, then simplify background, then tighten the reference crop. Only after that change models. Leave one checkable artifact from this step; do not start the next until it exists. 4) Save the character set: I tag the reference sheet in the asset library so the next episode starts from the same person. Leave one checkable artifact from this step; do not start the next until it exists.
Change one named variable per rerun: prompt, reference, camera, duration, model, ratio, or export crop. If two variables change together, a better output cannot be reused because nobody knows which fix worked.
Review gates and evidence
A viewer should order the shots without help, recognize the same character, and see no prop teleport between frames.
Keep source, approved wording, rejected version, correction, credit cost, and final crop next to the asset. The record exists so the next reviewer can reproduce the decision without a chat thread.
Failure diagnosis and retry ladder
Repair the reference before rewriting the prompt. If a transition fails, insert a short still instead of paying for a longer guessing shot.
Image retries have a ladder. Start with Agnes Image 2.5 for the cheap proof. Use SDXL Inpainting at 3 credits when only a masked region is wrong. Use Seedream 5.0 Lite or Nano-Banana 2 at 5 credits for a more complex rebuild. GPT Image 2 starts at 2 credits for Standard 1K and rises to 80 by quality and resolution. Name the failure before changing rungs.
Versioning and handoff
Hand off Canvas, reference IDs, prompt history, first/last frames, caption track, and the approved cut.
The folder is boring on purpose: source, approved reference, generation settings, caption file, platform cuts, QA screenshots, and one correction line. A reusable asset is boring in the right way.
Why this is not a generic answer
Editing is not detached from production. Object detection, masks, reference images, version history, Canvas dependencies, and the final generation set stay together. A standalone generator can invent a scene; it will not preserve the source asset, the rejected versions, and the reason the approved crop won.
Do not let a strong style erase story time, character state, or the audience's understanding of who did what. If the request only asks what consistent character ai video generator means, a search page is faster. This page is useful when someone must deliver three recognizable shots from one character under real constraints.
Frequently asked questions
How many shots should I test?
Three: close-up, medium action, and end frame.
The workflow is specific about its stopping point: it starts with character reference sheet and shot list and stops at three recognizable shots from one character. In imgmov, upload the source, lock it as a reference, set keep one ratio across shots, run one proof, and save the passing settings. If a reviewer cannot tell which version is approved, the workflow has failed even when the media looks good.
Why does clothing drift?
Long motion and multiple accessories. Fix the clothing set and remove competing details.
Image retries have a ladder. Start with Agnes Image 2.5 for the cheap proof. Use SDXL Inpainting at 3 credits when only a masked region is wrong. Use Seedream 5.0 Lite or Nano-Banana 2 at 5 credits for a more complex rebuild. GPT Image 2 starts at 2 credits for Standard 1K and rises to 80 by quality and resolution. Name the failure before changing rungs.
What is the cheapest test?
10 credits for a 5-second Agnes Video probe.
The check is not “does it look AI-nice?” It is: A viewer should order the shots without help, recognize the same character, and see no prop teleport between frames. Then the source, approved copy, rejected version, correction, and final crop stay in the same handoff folder.
Which models support end frames?
Agnes, Kling, Veo, and Seedance all support end-frame references.
Repair the reference before rewriting the prompt. If a transition fails, insert a short still instead of paying for a longer guessing shot. Change one named variable, keep the old version, and record the credit cost. If the same failure repeats, fix the reference or scope rather than asking the prompt for forgiveness.
When should I stop retrying?
After two correct-prompt retries fail the same trait. Rebuild the reference sheet instead.
Editing is not detached from production. Object detection, masks, reference images, version history, Canvas dependencies, and the final generation set stay together. A standalone generator can invent a scene; it will not preserve the source asset, the rejected versions, and the reason the approved crop won. The handoff is reusable only when the source, reference ID, prompt, model, cost, rejection reason, and approved cut are linked. That chain is what makes the next consistent character ai video generator task faster.