Hook
Whenever a new AI video model appears, my first instinct used to be simple: write a prompt, generate a few clips, and see what happens.
But after testing different AI video workflows, I realized that this approach doesn't tell me very much.
A good-looking demo doesn't necessarily mean a model will work well in a real project. What matters more is whether it can follow camera instructions, maintain visual consistency, handle references, and reduce the amount of regeneration required.
That's why I'm taking a more structured approach with Wan 3.0.
Instead of starting with random prompts, I'm preparing a small workflow that will help me evaluate how it performs in practical video projects.
What I Want to Test with Wan 3.0
For a new AI video model, I'm less interested in generating one spectacular clip and more interested in workflow reliability.
The areas I want to explore include:
- Image-to-video consistency
- Reference-based generation
- Camera movement
- Character preservation
- Multi-scene continuity
- Prompt adherence
- Editing flexibility
These are the things that usually determine whether a model becomes part of my regular workflow or remains something I only use for experiments.
How I'm Preparing My Workflow
Step 1 — Build a Small Test Story
Instead of testing unrelated prompts, I'm preparing one simple story.
For example:
Scene 1: A woman enters a quiet coffee shop.
Scene 2: She walks toward the counter.
Scene 3: The camera follows as she moves toward a window.
Scene 4: She sits down and looks outside while rain falls on the street.
The story is intentionally simple.
I'm not trying to test complex storytelling yet. I want to see whether the same character, environment, lighting, and overall visual direction can remain consistent across several shots.
Step 2 — Prepare Reference Assets
Next, I organize a small reference library.
It includes:
- Character reference
- Environment image
- Clothing reference
- Lighting example
- Visual style reference
Preparing these assets first means I don't have to describe every visual detail inside the prompt.
Instead, the prompt can focus more clearly on action and movement.
Step 3 — Prepare a Reusable Prompt
My starting prompt might look something like this:
A woman walks slowly through a quiet modern coffee shop.
Warm afternoon light enters through large windows.
Natural body movement.
Cinematic atmosphere.
Realistic pacing.
Camera: slow tracking shot from behind.
Once I'm ready to evaluate Wan 3.0, this is the kind of controlled prompt structure I want to start with.
I deliberately avoid making the prompt too complicated.
The goal is to establish a baseline first.
Step 4 — Test Camera Movement Separately
Next, I change only the camera instruction.
For example:
Test A
Slow tracking shot from behind.
Test B
Slow cinematic dolly from left to right.
Test C
Static medium shot with subtle handheld movement.
The subject, environment, lighting, and action remain unchanged.
Only the camera direction changes.
This should make it much easier to evaluate how accurately the model follows camera instructions.
Step 5 — Test Character Consistency
After camera movement, character consistency would be my next test.
I'd generate several scenes using the same character reference while changing only:
- Action
- Camera angle
- Environment position
- Shot distance
Then I'd compare details such as:
- Facial identity
- Hairstyle
- Clothing
- Body proportions
- Overall visual style
For multi-scene storytelling, these details matter much more than whether one individual frame looks impressive.
Step 6 — Keep a Simple Evaluation Checklist
For every generation, I'd record the same information:
- Did it follow the requested action?
- Was the camera movement correct?
- Did the character remain consistent?
- Did the environment remain stable?
- Were there obvious motion artifacts?
- How many attempts were required?
- How much editing would the result need?
This turns model testing into something measurable instead of relying entirely on first impressions.
Use Cases I Want to Explore
Short-Form Storytelling
Can multiple generated shots feel like parts of the same story?
Product Marketing
Can the same product remain visually consistent while the camera position and environment change?
Social Media Content
Can one creative concept be turned into several short-form variations without rebuilding everything?
Storyboarding
Can simple ideas be converted into useful moving previews before committing to a larger production?
Creative Pre-Visualization
Can reference assets and camera prompts provide enough control to test a scene before traditional production?
These are the scenarios where I think workflow reliability matters most.
Why I Prefer This Testing Approach
One problem with testing new AI models is changing too many things at the same time.
If I change the prompt, reference image, camera movement, style, and scene simultaneously, I can't tell which change actually improved the result.
That's why I prefer a controlled workflow:
- Start with the same references.
- Use the same baseline prompt.
- Change one variable.
- Generate again.
- Compare the results.
- Record what changed.
It isn't as exciting as immediately trying an extremely complicated cinematic prompt, but it gives me much more useful information.
Final Thoughts
What interests me about Wan 3.0 isn't simply whether it can generate an impressive demo.
I want to know whether it can fit into a repeatable creative process.
Can it follow camera directions reliably? Can it maintain a subject across different shots? Can references reduce prompt complexity? And can it reduce the number of generations needed to get a usable result?
Those are the questions I'll focus on when evaluating Wan 3.0.
Until then, I'm preparing the references, prompts, and test cases so I can evaluate the workflow—not just the first generation.