How to keep the same character in AI videos

What is character consistency in AI video?

Character consistency means the same face, outfit and proportions from one shot to the next. Drift is what you see when it fails: the hair changes colour, the jacket loses a pocket, or the lead turns into a different person between two clips. Faceless story channels run into this first, because a story only works if viewers believe it is one person throughout.

This page covers why drift happens, the fixes that several vendors independently describe, and a dated table of what Google, Higgsfield, Kling and Runway each document. We read those vendor pages on October 22, 2026. Vendor limits change, so dates matter, and the last section lists what we did not read or test. Nobody can promise views, and no tool promises an identical face.

Why does the character change between shots?

Because most generators start over each time. Here are four vendors describing the cause in their own words, all read October 22, 2026:

• Runway: "by default, AI generates from scratch every time." It adds that video "requires maintaining consistency across multiple frames, which is harder than single images."
• Higgsfield: "Most AI generators process each prompt independently. They are optimizing for a good single image, not for memory of the last one," and "there are infinite faces that match 'tall woman with dark hair and brown eyes.'"
• Kling: "AI models do not inherently recall past generations."
• Higgsfield again, on references: an image "anchors a single generation but drifts across many separate ones."

Read together, they say two things. A text description fits many faces, so the model picks a new one each run. And even a reference image only helps inside the generation you attach it to unless the tool lets you save it as a reusable asset. These are vendors describing their own products, which is worth remembering, but the diagnosis is the same everywhere.

What do the sources agree on?

Seven habits show up across the vendor pages we read. Each is a suggestion from sources, not a guarantee, and we did not run our own benchmark.

On that last row, two public creator comments describe the same habit. One says they used Gemini to set the look of both characters first and then "treated them like actor headshots and reused the exact same files every time." The same person says "I threw away most of what I generated." That is one creator's account, not data, but it fits the vendor advice: reuse the files, discard the drift.

How many reference images do you need?

It depends on the tool, and the numbers have moved this year. Kling's older guide, dated November 2025 in search results, describes Elements with 1 to 4 images on model 1.6. Its 2026 guide describes a multi-image element of 2 to 4 images, and up to 7 images or elements in a video request without a clip input. Google's Gemini API page for Nano Banana Pro lists up to 5 character images and 14 references in total. The right count for you is whatever the tool you use accepts, so check its page before you plan the shoot.

More is not always better. Higgsfield says a smaller set of clean, varied photos outperforms a large set of inconsistent ones, and that repeating the same pose across all photos hurts. Runway recommends even, natural lighting and neutral expressions for its references. Variety of angle matters more than volume.

What is a character sheet and how do you make one?

A character sheet is one image, or a small set, that shows your character from several angles in the same outfit and style. You make it once, approve it by eye, and then attach it to later generations so you are not asking the model to remember. Google's codelab walks through building one from a single archive image and then generating illustrations from the sheet and a prompt. We read the codelab page, not the notebook, so we cannot say how well it works for your style.

For a story channel, the practical version is a short photo shoot: the approved portrait, a side or three-quarter view, a full-body view and, if the outfit has a back detail, the back. Google's image page gives the same loop in plain words: for different angles, include previously generated images in later prompts, and for complex poses include a reference image of the pose.

Should you describe the character in every prompt?

This is where the vendors contradict each other, so here is the evidence. Runway's guide says to remove character appearance descriptions from your prompts when you use a reference. Kling's guide says to write a master description and repeat the same keywords in the same order every time. We could not find a source that settles it.

If you want a deeper look at fixed, structured prompt text for shots, see our guide to JSON prompts for AI video. This page stays on identity.

Is image-to-video or text-to-video better for a consistent character?

Image-to-video is the safer starting point, according to every vendor that addresses it. Higgsfield's Seedance help says to start with image-to-video at 720p using a sharp, front-facing, well-lit reference, and that it is "more predictable than text-only and uses fewer credits while you learn." Runway advises generating a still first and then converting it to video. Kling says its Elements feature gives more creative control than text-to-video. Higgsfield also says a reference holds a face within a single generation, and that to keep the same character across separate generations you save it as an Element.

What does each vendor document about character references?

Here is the dated side-by-side. A "not documented" cell means we did not find the figure on the vendor's own page; it does not mean the product lacks the feature. These are vendors describing their own tools, and several of them sell what they describe.

Google's Veo post from January 2026 says identity consistency is "better than ever" for reference-based video and gives no image count. We do not say Veo is current in the Gemini API, because that page names a different video model and says the preview was shut down.

How do you keep two characters apart?

Harder than one. Runway advises building each character separately first and making them visually distinct, with different hair, outfit colours and silhouettes, so the model has fewer chances to blend them. Higgsfield says a Soul ID holds one person and points to Elements when a scene needs two or more consistent characters. Kling's Element Library allows more than one element per request, within the limits in the table. If your story has a recurring supporting cast, give each member one clear visual hook before you generate anything.

Can you use a real person's photo as a reference?

Only with permission. Higgsfield's Soul ID help says to upload photos only of yourself or of someone who has given you permission to use their likeness. Reference uploads can also be refused by a tool's moderation; we saw a public thread titled with that complaint, but did not read its replies, so we do not state a cause. Many faceless channels use a designed character they own, which avoids the consent question and makes it easier to describe what is in the video. Our faceless AI video channel guide covers the platform side of AI content.

A workflow you can run this week

Here is a tool-agnostic order, with Nano Banana Pro then Seedance on Higgsfield as the worked example because the course stack uses them. Last checked October 22, 2026. For the full pipeline from story to posting, see how to make AI story videos.

1. 1Generate several candidates of your lead and pick one. Approve the portrait by eye before anything else.
2. 2From that image, make two or three more angles, feeding the approved image back in as a reference each time.
3. 3Write a short fixed text block for outfit, one signature item and style. Save it in a file and never retype it.
4. 4Generate the still for each shot with the same references and the same block, and check each against your portrait.
5. 5Animate from the still, not from text alone. Keep clips short.
6. 6When a clip drifts, change one thing and re-run: the reference, the block or the motion prompt, not all three.
7. 7Throw away clips that look slightly off, then join the keepers in an editor.

Expect an honest ceiling. Higgsfield itself says to expect "clearly the same person" rather than a pixel-identical face. Another creator we read says nano banana pro gave great consistency between scenes but that "there are still some slight inconsistencies here and there." Budget time for re-rolls.

Where does AI Video Empire fit?

AI Video Empire is the course we sell for animated story videos with a designed character and no face on camera. One of its modules is about characters and consistency, and another covers debugging generations by changing one thing per run. It is a one-time payment of $27 with lifetime access. The tools it uses, Nano Banana Pro, Higgsfield with Seedance, Claude Code and CapCut, each have their own paid plan or credits. We sell it, so weigh this section accordingly. Nobody can promise views, and this page teaches the general method from public sources.

What we read and did not test

We read the Google, Higgsfield, Kling and Runway pages in the sources list on October 22, 2026. Google's API pages showed a last updated date of 2026-10-22; for Higgsfield, Kling and Runway the dates come from search results, not the page body. We did not generate an image or clip for this article, so nothing here says "we tested". We did not read the codelab notebook, Runway's current video reference limits, or any Veo reference count on a Google page. Other tools exist and we did not research them. Creator comments are single accounts, not data. Vendor limits and plans change, so re-check each page before you buy.

Patel, O. (2026, October 22). How to keep the same character in AI videos. Rushilo. https://rushilo.com/blog/consistent-ai-characters

About the author

Om Patel

Founder, Rushilo

Om Patel is the founder of Rushilo, a desktop video editor driven by the user's own AI, and of BigIdeasDB, a research platform that reads real complaints and reviews to help founders validate products. He writes Rushilo's research articles with the same method: primary sources, dated numbers and the words real users write.

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