Consistent Characters in Generative AI Video
The number-one problem in AI filmmaking is a character whose face changes every cut. This is the exact step-by-step method — reference codes, character weights, and seeds — we use to make it stop.
Ask any AI filmmaker what breaks the illusion fastest and the answer is the same: the character morphs. A face shifts between shots, a jacket changes color, a hairstyle reinvents itself mid-scene. Audiences read this instantly as "fake." The fix is not luck or a better prompt sentence — it is a repeatable technical discipline, and this tutorial walks through it in order.
Why models struggle with continuity
Generative models have no persistent memory of your character. Every text-to-video generation re-imagines the subject from the prompt, so unless you anchor identity to a fixed visual reference, the model is free to reinterpret it each time. The entire solution comes down to giving the model something concrete to copy from — and never letting it improvise the character from words alone.
The six-step consistency method
- 01
Draft a clean hero image
Generate a front-lit, neutral-background portrait of your character in Midjourney at
--ar 2:3. A plain background forces the model to fix on face and wardrobe. This image is your reference asset for everything downstream. - 02
Lock the character description
Write a fixed description block — age, build, exact wardrobe, distinguishing features — and reuse it verbatim in every prompt. Consistency is enforced in language before any reference tag does its work.
- 03
Apply --cref with tuned character weight
Use
--cref [image URL]to carry identity into new compositions. Tune--cw: a high value (~100) locks face and clothing; a lower value (~30–50) holds the face while letting you change wardrobe deliberately. Set it according to whether the costume should travel. - 04
Generate every angle from the same seed
Produce the wide, the medium, and the close using the same reference and a fixed
--seed. Reusing the seed keeps lighting and rendering coherent so the shots read as the same scene, not three different days. - 05
Port the reference into Runway as a start frame
Take your locked frame into Runway Gen-3 and use image-to-video, not text-to-video. The starting frame anchors the character's identity as motion begins — this is the step that stops faces from morphing mid-clip.
- 06
Fix drift with regional retries
When clothing shifts or a face mutates, do not accept it — re-roll that specific generation. Lower camera motion, simplify the prompt to one action, and regenerate from the stronger reference until the take holds.
The Midjourney-to-Runway handoff
The pivotal move is porting a locked Midjourney frame into Runway as a starting image for image-to-video. This is the difference between a character who holds and one who melts: the start frame fixes identity before motion begins. Text-to-video for a recurring character is fighting the tool; image-to-video works with it.
Fixing drift when it happens
Even with a strong reference, some takes drift. The professional habit is to treat a drifted generation as a discard, not a compromise — lower the camera motion, simplify the prompt to a single clear action, and re-roll from the stronger frame. Budget for this iteration; commonly reported usable-take rates sit near one in three, and chasing the clean take is cheaper than accepting a broken one. The broader motion-control context lives in our generative video workflow.
Master this and you have solved the single hardest problem in AI video. From here, the work becomes directing — which is exactly where you want to be. For the cinematography vocabulary that elevates each shot, continue to the cinematic prompt guide.