Consistency across AI shots comes from three habits: supply the same reference images every time, design shots so small differences do not show, and make the final selection in the edit rather than trusting any single generation. No method guarantees an identical subject in every frame, so plan for some shots to be rejected and regenerated.
Why subjects drift
Most generation tools produce each clip from a fresh interpretation of your prompt. Words alone are ambiguous: “a woman in a green jacket” can become hundreds of different people. Details that matter commercially, such as a logo, a bottle shape or a stitch pattern, are the first to drift. This is one of the central issues covered in the real limitations of AI-generated video.
1. Use reference images, not just descriptions
Current tools increasingly accept reference images to anchor a subject. As examples, Google’s documentation says its Veo model can take up to three images of a single person, character, or product to preserve the subject’s appearance, and that its Gemini Omni model supports character consistency across up to six reference images. Limits and model names change often, so check the current documentation of whichever tool you use. These details are accurate at the time of writing (November 2026).
Good reference sets share a few traits:
- Consistent lighting, neutral background, and sharp focus.
- Several angles (front, three-quarter, side) of the same subject.
- For products, real packshots rather than generated ones, so the reference is the true item.
2. Design shots that forgive variation
- Keep faces and labels away from the camera when you can. Over-the-shoulder, hands-in-frame and silhouette shots are easier to match than long close-ups.
- Limit what must match. A product hero shot needs accuracy; a background plate does not.
- Hold constant what you can. Same time of day, same wardrobe, same lens language across shots reduces the number of variables the model has to reproduce.
- Keep clips short. A few seconds per shot gives fewer chances to drift than long takes.
- Cut on action. Movement hides small differences at the cut point.
A written storyboard or animatic helps here, because you can see which shots are most demanding before generating anything. See how to storyboard a commercial.
3. Fix it in the edit
- Generate several candidates per shot and select the closest match, rather than accepting the first.
- Use a conventional colour grade across the sequence so differences in tone read as one look.
- Replace critical details (logos, label text, product screens) with real artwork composited in post, instead of relying on generated text.
- Use traditional retouching or compositing for the final hero frames.
Review checklist
| Check | What to look for |
|---|---|
| Identity | Face, hair, skin tone and build match the reference |
| Wardrobe | Colours, fastenings and accessories unchanged |
| Product | Shape, proportions, colour and label match the real item |
| Brand marks | Logos and text are legible and exact |
| Scale and hands | Product size relative to hands is plausible |
| Continuity | Lighting direction and setting stay coherent |
Limits to keep in mind
Reference images improve matching; they do not make it exact. For commercial products, the final result must still look like the product customers will receive, which matters under Australian consumer law on misleading representations. Treat this as general information, and seek professional advice for specific claims. For the product-specific workflow, see AI product videos.
Where Radiance Impact fits
Radiance Impact’s Studio produces AI commercials and product films, with reference preparation, shot design, editing and post-production handled as one workflow. Learn more at /studio or contact us to discuss a project.
Sources
- Use reference images to guide video generation, Google Cloud documentation
- Video generation with Gemini, Google AI Studio documentation
