One of the most frustrating problems I've encountered with AI video generation is character inconsistency. You create a video with someone's face in the first frame, and by the third second, their appearance has shifted. Eyes change shape.
Skin tone fluctuates. Clothing details morph into something different. It's the uncanny valley of video generation—everything looks almost right, except the human element feels wrong.
This was my primary blocker for months. I could generate good-looking video content, but the moment a person appeared on screen, the inconsistencies became obvious. Clients would reject videos outright because "the character doesn't look like the same person throughout." For content creators trying to build a cohesive brand or tell a continuous story, this was a deal-breaker.
Then I started using Seedance 2.0, and the character consistency issue largely went away.
Why Character Consistency Matters So Much
Let me explain why this problem bothered me more than other AI video limitations. You can forgive awkward backgrounds, slight spatial distortions, or even weird physics in certain situations. But when a person's face or appearance changes mid-video, it breaks the narrative contract with the viewer.
If I'm creating a product testimonial and the person's appearance shifts halfway through, the viewer notices. If I'm building a character-driven narrative and the protagonist looks different in each scene, the story falls apart.
For e-commerce creators, fitness instructors, educators, and anyone doing personal brand content, character consistency isn't optional—it's fundamental.
My Previous Experiences With Character Drift
Before Seedance 2.0, I'd tried other AI video tools that advertised consistency features. The results were... mixed.
I remember one project where I was creating a fitness instruction video. I used the same reference image of the instructor throughout, thinking that would ensure consistency. Instead, the model seemed to "interpret" the instructor differently in each generation. In one clip, they had a lean face. In the next, the face was rounder.
The scar on their left cheekbone appeared and disappeared. It was like watching the same person slowly shift into an alternate version of themselves.
I tried being more specific with prompts. I'd describe every detail: "The instructor has a sharp jawline, a specific hairstyle, a tattoo on their right forearm." The model would acknowledge all of this... in the first few frames. Then, gradually, the appearance would drift.
The manual solution was to regenerate the same clip multiple times and cherry-pick the best frames, then edit them together. This was time-consuming and didn't scale—for a 15-second video, I might need 30+ generations to get consistent shots that I could piece together.
How Seedance 2.0 Handles Consistency Differently
When I first tested Seedance 2.0's consistency capability, I was cautious. I'd been disappointed before. But the approach is genuinely different.
Instead of just accepting a text description and hoping the model maintains it, Seedance 2.0 lets you reference actual images or video footage of the character. The model uses those visual references as anchors for consistency.
For example, instead of describing an instructor as "fit with brown hair and a specific look," I simply upload a photo of the actual instructor. The model then uses that image as a reference guide, ensuring the generated character maintains those visual qualities throughout the video.
This is a fundamental shift from "describe what you want" to "show me what you want." It turns out showing works much better than describing.
My Real-World Test: Fitness Tutorial Series
I decided to run a realistic test. A local gym hired me to create a series of five 30-second exercise tutorial videos featuring their head trainer. Each video would show the trainer performing a different exercise, demonstrating proper form.
Normally, this would be a nightmare from a consistency perspective. Five videos, each with extended shots of the same person, means there are 50+ seconds of footage where the trainer's appearance needs to remain stable.
Here's what I did:
Step One: Gathering Reference Materials
The gym provided high-quality photos of the trainer from different angles and lighting conditions. I also shot a short video of them demonstrating one of the exercises (about 20 seconds). I had visual reference material that showed the trainer's appearance clearly.
Step Two: Using References for Consistency
When generating each tutorial video, I uploaded the reference photos at the beginning of my process. I didn't just describe the trainer; I gave Seedance 2.0 actual visual examples.
For example, my prompt was: "Create a 30-second fitness tutorial. Feature the instructor from @image1 (frontal shot) and @image2 (side angle) demonstrating a bicep curl with proper form. Show the movement from multiple angles, maintaining the trainer's appearance and style throughout."
Step Three: Comparing Consistency Across Videos
Once I had all five videos generated, I reviewed them for consistency. Did the trainer look the same across all five videos? Did their appearance remain stable within each 30-second clip?
The results were remarkably consistent. I'd say there was 85-90% consistency across all five videos. The trainer's face, build, clothing, and overall appearance remained recognizable and stable throughout. There were minor variations—which is realistic and actually adds authenticity—but no jarring shifts or glaring inconsistencies.
Why the Photo Reference Method Works Better
The reason this approach is more effective than text-based descriptions is intuitive once you think about it. The model isn't trying to interpret your language and guess what a "sharp jawline" or "athletic build" means. Instead, it's looking at a visual example and saying, "okay, I need to maintain these specific visual characteristics."
It's the difference between describing a painting and showing a painting.
I've noticed that the quality of the reference material matters. Higher-resolution photos with good lighting produce better consistency than low-quality or heavily filtered images. This makes sense—the model has more visual information to work with.
Additionally, reference images showing the character in different expressions or angles help the model understand their appearance more completely. A profile shot plus a frontal shot gives more information than just one angle.
Limitations Still Exist
I want to be honest: Seedance 2.0's consistency isn't perfect, and there are specific scenarios where it struggles.
Very complex scenes with multiple characters sometimes produce inconsistencies in secondary characters. If my video features the main instructor plus gym members in the background, the background people might not maintain perfect consistency. But the primary character referenced at the start usually stays stable.
Extreme changes in clothing or styling can also introduce drift. If I ask the model to generate the same character in different outfits, the consistency sometimes falters. This makes sense—I'm asking for variation—but it means I can't freely transform the character's appearance within a single video.
Additionally, subtle features like specific scars, tattoos, or distinct markings might not persist across a full 15-second video. The model captures the general appearance well, but fine details sometimes fade.
The consistency also depends heavily on the prompt clarity. If I write a vague prompt like "continue the character," the model is less consistent than when I'm specific: "maintain the trainer's appearance from @image1, showing them in the same gym setting, performing a different exercise."
Time and Iteration Savings
The practical benefit is significant. Before discovering this approach, creating a multi-video series with consistent characters meant:
- Generating dozens of clips to cherry-pick consistent ones
- Spending hours on manual editing to blend inconsistent frames
- Often accepting lower-than-desired quality because perfect consistency was unrealistic
Now, the workflow is:
- Prepare good reference images
- Write clear prompts that reference those images
- Generate videos
- Minor touch-ups if needed
- Deliver
It's faster and the base quality is higher because I'm not settling for the "least inconsistent" option from 20 bad generations.
Applications Beyond Fitness
Since that initial project, I've used this consistency approach for:
Educational Content
A teacher wanted a series of short explanation videos with the same instructor. Consistency was crucial for building student familiarity. Using reference photos of the instructor, I could generate all the videos with the same person appearing stable across all of them.
Brand Ambassador Videos
A product company wanted multiple promotional videos featuring the same person. Without good consistency, it would feel like different people were endorsing the product. With visual references, the brand ambassador remained recognizably the same across all promotional materials.
Character-Driven Narratives
For a short film project, I needed a lead character to appear in multiple scenes. Using reference images from the first scene, I could maintain their appearance in subsequent scenes generated separately, then edit them together into a cohesive narrative.
Why This Matters for Content Creators
The consistency breakthrough matters because it removes a significant technical barrier. For years, AI video generation struggled with what should be simple—making the same person look like the same person throughout a video. It sounds basic, but it was a persistent problem.
Now that Seedance 2.0 handles this reasonably well, creators can focus on composition, storytelling, and creative direction instead of worrying about whether their character's face is going to morph into someone unrecognizable.
This is especially important for creators building personal brands or creating educational content. Consistency builds familiarity. Familiarity builds trust. And trust builds audience.
What I Still Do Manually
I should note that I'm not fully replacing video production with AI. For content where the character is the primary focus (like an extended interview or a detailed skills demonstration), I still augment Seedance 2.0 generations with traditional editing techniques.
For example, with the fitness videos, I did add overlays, transitions, and editing effects afterward to enhance the polish. The AI generation handled the character and their movement; I handled the post-production elements that elevated the overall production value.
Think of Seedance 2.0's consistency as solving the character stability problem, but not the entire production problem.
