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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsTo keep a character consistent across AI-generated video, anchor each shot to the same clean reference image, plan the sequence shot by shot, and prompt for the movement that changes—not a fresh description of everything that should stay the same. Generate manageable clips, then review them for visual drift and revise the reference or simplify the shot when needed. These steps reduce ambiguity, but current tools do not guarantee identical characters in every frame.
Why characters change between AI video shots
Each generation can reinterpret a face, hairstyle, outfit, proportions, or surroundings. A long text description alone may not prevent those changes. In image-to-video generation, the starting image is especially important: Runway says it supplies composition, subject, lighting, and style, while Google says the source image underpins character detail, lighting, and overall style. If the image has a blurry face or malformed hands, Runway warns those defects may become more pronounced in the video.
References and shot planning can help guide a model, but they are controls rather than an identity lock. Google notes that a Veo seed does not guarantee deterministic results, although it may improve consistency slightly. No universal success rate or independent head-to-head continuity winner is established by the cited product documentation.
A practical workflow for visual continuity
- Choose one clean character reference. Select or create an image with the character’s defining traits clearly visible. Check the face, hair, clothing, and proportions before using it as a starting point; a prompt is not a reliable way to repair a weak reference.
- Plan the sequence as separate shots. For each shot, note the reference image, setting, wardrobe or story-state changes, framing, and intended action. This simple shot list makes intended changes easier to distinguish from accidental drift.
- Reuse supported references. If the model offers reference-image guidance, use the same character reference across shots. Follow the current product’s input limits and model-version requirements.
- For image-to-video, describe motion. Explain what the subject does and how the camera or environment moves. Avoid restating a full inventory of details already visible in the image unless you want those details to change.
- Generate one manageable scene at a time. Begin with a simple action, then add motion or style instructions incrementally if the result needs refinement. When supported, carry a sequence forward from a completed clip’s last frame.
- Inspect the result before moving on. Compare the face, hair, clothing, body proportions, object placement, lighting, and location with the reference and shot plan. If the output drifts, correct the reference or divide an overcomplicated shot into simpler parts.
How to prompt an image-to-video shot
When the still image already establishes the character and scene, make the prompt about the change over time. For example: “The woman turns toward the window and raises her right hand; the camera slowly pushes in; curtains move gently in the breeze.” This describes action, camera movement, and environmental motion without asking the model to reinvent the character’s appearance.
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If a detail should change—such as a character taking off a jacket—state that change explicitly and make sure it matches the story. For a static detail that should remain stable, rely on the reference image and keep the shot simple. Runway and Google both recommend focusing image-to-video prompts on motion rather than repeating what the image already shows.
Reference and continuation controls in Veo 3.1 and Runway Gen-4
| Tool and documentation | Reference and continuity controls | Documented clip details | Important limitation |
|---|---|---|---|
| Google Veo 3.1 | Google documents up to three reference images to guide a person, character, or product. It also documents first- and last-frame direction and video extension. | Google documents 4-, 6-, or 8-second videos. Reference-image use requires an 8-second video. | Settings and constraints differ by model variant. A seed does not guarantee determinism. |
| Runway Gen-4 | Runway describes references for maintaining consistency across subjects, styles, and locations. | Runway’s guide describes 5- or 10-second generations based on an input image and text prompt. | Documentation describes intended controls, not guaranteed identity consistency. |
These figures describe documented model capabilities, not a measure of how often a character stays identical. Check the current model variant, resolution, duration, input constraints, and availability for your location before building a workflow around a feature.
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When continuity breaks, change the reference or the shot
- The face or hair changes: Use a clearer reference where those features are visible, and reuse it for the affected shots.
- Clothing or proportions drift: Check whether the reference clearly shows the intended outfit and silhouette. Separate a wardrobe change from other complex action if the model is changing too many things at once.
- The scene or lighting shifts: Reuse a scene or style reference where the tool supports it, and keep the shot plan explicit about what should remain in place.
- The action becomes confusing: Simplify the prompt to one main action and a clear camera movement. Add complexity only after the basic shot works.
- A longer sequence loses continuity: Where supported, use the preceding generation’s final frame as the next input, or guide the result with first- and last-frame controls. This can help establish a visual handoff, but it is not a guarantee that every detail will match.
Check that the tool is available before choosing it
Product availability changes. OpenAI’s Sora product page says Sora is no longer available as of April 26, 2026, so it should not be recommended as a currently available option. For any video model, verify availability in your geography and confirm its current version and input limits rather than assuming older feature descriptions still apply.
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