AI Video's Character Consistency Problem & How to Fix It

If you've spent any time experimenting with AI video tools like Runway or Google's Veo, you've seen the magic. You've almost certainly seen the glitches, too.

Say, you prompt a character, and from one scene to the next, their face subtly morphs. By the time you generate your tenth clip, the character will look nothing like the initial reference. In other scenarios, objects you never mentioned in the prompt may mysteriously appear in the picture.

These aren't just minor bugs; they're consistency failures that can completely derail your narrative.

When a large language model invents things, we call it "hallucination." In the realm of AI video production, the phenomenon manifests itself in unwanted character and setting changes.

At YOPRST, a video production company I collaborate with, such hallucinations remain our biggest roadblock, turning the "fast and easy" promise of AI into hours of re-rolling and post-production fixes.

That’s why I decided to dig deeper, uncovering the true cause of AI tools’ performance issues and proposing practical ways to combat them.

The Problem: Unwanted "Extras" and Identity Drift

The two most common challenges faced by AI video creators are identity drift and hallucinations stemming from prompt deviations. This is what it actually means in practice:

• Identity drift. Early AI video models treated each new scene (or even each new frame) as a separate task. The result? Your hero's hair color might flip, or their outfit might change mid-shot. It's like asking a dozen different artists to draw sequential frames of a cartoon without giving them a reference sheet.
• Prompt-related hallucinations. The other common issue is the AI adding things you never asked for. You prompt a cat sitting on a sofa, and the AI throws in a random lamp, a potted plant, or even a second cat. This happens because artificial intelligence follows patterns it has learned from training data (e.g., "sofas often have lamps next to them") and "helpfully" adds extra objects in. Simply put, the model isn't sticking to your script. Google even highlighted "stronger prompt adherence" as a key feature in its Veo 3.1 model, admitting this was a major struggle in earlier versions. When prompt adherence is weak, you get those off-script artifacts. Annoying. 🙄

For any serious use case—marketing, storytelling, or branding—consistency is non-negotiable. Your mascot has to stay on-model. Your audience needs to know who the protagonist is. We need our AI to remember what it did in frame 1 all the way to frame 1000.

Why Do AI Videos Lose Consistency?

The issues I described above aren't random; they stem from the fundamental architecture of most video models:

• Frame-by-frame generation. Until very recently, AI models generated video as a sequence of loosely connected images. The AI would create frame 1, then look at it to create frame 2, and so on. As VentureBeat described it, the model "treated each frame as a separate creative task," lacking a persistent “mental model” of your character and drifting over time.
• Lack of a "world model." A human filmmaker or a game engine understands 3D space. If a car is on the character's left, it stays there when the camera angle changes. AI video models don't inherently get this, lacking true understanding of consistent space and time. They learn from flat, 2D video clips. This can lead to bizarre artifacts—like a motorbike hovering while the background moves—all because the model misinterpreted what a “camera panning” shot actually represents in its training data.
• Diffusion models’ tendency to fill in the gaps. Many modern AIs (like Stable Diffusion) are diffusion models by design. They start with random noise and refine it step-by-step to match your prompt. By their very nature, they are generative. They want to fill in ambiguous details with plausible data. If your prompt is simply "a scene from a park," the model's biases from its training data will dominate the output. AI "knows" parks often have benches, dogs, and other people, so it adds them. AI's not being malicious here; it's just trying to create a complete picture. Without strong prompting, the default behavior is to embellish.
• Training data and compute limits. Let's be real: maintaining consistency is also a resource-hungry problem. Generating a whole, coherent video sequence at once requires vastly more VRAM and compute than generating one frame at a time. Furthermore, the training data itself is full of inconsistencies (think of movie continuity errors). The AI might learn that it's "normal" for a character's appearance to change slightly between cuts because it saw that exact thing in its training data.

In its current state, generative models are a perfect storm, despite the fact that content creators greatly benefit from AI in video production. Artificial intelligence has no long-term memory, no real-world physics, and an overeager imagination, all running on a frame-by-frame system. Without expert guidance from humans, AI video creation experiments tend to fall miserably.

The New Fixes: How Runway and Veo Are Getting Smarter

The good news? Gen AI companies are racing to solve the problem. The latest models from Runway (Gen-4) and Google (Veo 3.1) have new features specifically designed to kill identity drift and hallucinations. These include:

• Reference images. Both platforms now let you provide reference images before generating actual videos. Google's Flow interface (using Veo) calls them "ingredients." Runway Gen-4 lets you upload a subject and generate new videos of them. By providing reference images, you are essentially instructing the AI, "See?" This is my character. Whatever you do, make them look like this." The model probably uses methods like IP-Adapter or other techniques to add information from the reference image into the diffusion process, which helps keep the results on track and minimizes errors.
• Persistent visual memory. The latest models from Runway have a persistent memory. The team explains the functionality as the system creating a character once and then rendering it from various perspectives while preserving its essential characteristics. This is an enormous leap in architecture. Now, one artist (the AI) has a model sheet pinned to their desk instead of a dozen artists drawing blind.
• Stronger prompt adherence. As I mentioned previously, models like Veo 3.1 are being explicitly trained to actually listen to you. It is much less likely that the model will hallucinate a random chair if your prompt is, "A single person standing in an empty room." This gives you more control over locking down the scene and eliminating ambiguity, especially when paired with more detailed controls (such as defining camera movements).
• Better model architecture. At a deeper level, the R&D teams of Gen AI companies concentrate on new architectures. These include 3D-aware models that use representations like Neural Radiance Fields to comprehend the scene in 3D, temporal diffusion models that work on a whole clip at once, and recurrent connections that give the model a true "memory" (like an RNN or Transformer) that transmits information from frame N to frame N+1.

Your Toolkit for Creating Consistent AI Videos

The "how" is important, even with newer, more sophisticated models. Consider the AI as a junior developer: quick and intelligent, but in need of precise specifications.

Here are the practical techniques you could use to wrangle better results.

1. Use reference images (seriously, always)

This strategy is your most powerful weapon:

1. Generate a high-quality "master" image of your character first (using Nano Banana, Midjourney, Stable Diffusion, etc.). Get a clear, front-facing portrait.
2. Feed this image into Runway's or Veo's reference/ingredient feature
3. Use this same reference image for every single shot you generate with that character

Without a reference, the face drifts. With one, it stays remarkably stable.

2. Write hyper-specific prompts

Don't give the model room to guess. Be explicit. Use code blocks for your prompts to keep them organized.

❌ Here’s an example of a bad prompt:
A man walking in a park.

Basically, this is an invitation for hallucinations. What man? What park? What's in it?

✅ And here’s an AI video prompt done right:
A wide 16:9 shot of a man in blue jeans and a red jacket with short brown hair. He is walking on a paved path in a dense pine forest. The scene is misty and quiet. The frame contains *only* the man and the trees. No other people, no animals, no benches. Style: cinematic, moody lighting, hyper-realistic.

If the tool supports it, use negative prompts to explicitly ban things like extra characters or blurry frames.

3. Use custom-trained models

This is the next tier. You can customize your own model for recurring characters or brand mascots. In the open-source world (Stable Diffusion), this step involves training a low-rank adaptation (LoRa).

You feed the AI ~30 images of your character from different angles, and it "learns" a new, unique token for them (e.g., my-character-token). From then on, you can just prompt: my-character-token riding a skateboard ...and the custom model will generate your specific character.

4. Plan your shots and edit

You can never rely on AI video platforms for longer, continuous shorts. Instead, you should think and act like a true filmmaker:

1. Generate shorter, 4-5 second clips (one for each "shot")
2. Reuse your reference image and core prompt for each clip
3. Stitch these clips together in a video editor (like Premiere or DaVinci Resolve)

The cuts hide any minor drifts and "reset" the AI's consistency for every new shot. You can also do a final color grade or apply a style filter to the entire video to "glue" all the shots together visually.

Creating AI Videos: From Glitch to Feature

Achieving consistent quality of AI videos is not just a “content creator” problem—it's a fascinating technical challenge. Taming a generative model's output is becoming a core skill, much like debugging code. You test, see a glitch (a hallucination), and then adjust your "instructions" (the prompt) or the system design (the reference image) to fix it.

The struggle for consistent AI video is real, but it's getting solved. Understanding why models fail (no memory, 2D thinking) and how to guide them (references, LoRAs, tight prompts) can help us bridge this gap. From my experience, when you finally get that perfect, consistent shot, it feels like magic. So go experiment. Grab a reference image, tighten up your prompts, and show that AI who's boss!