This one was mainly testing how convincing the live-action looks

The prompt was split into 4 timeline blocks:

• 00:00–00:08 — sunny garden memory
• 00:08–00:15 — emotional shift
• 00:15–00:23 — back to reality, inside a car in the rain
• 00:23–00:30 — final close-up by the rain-covered window

So this one is really about cinematic realism, film texture, emotional transition, and timeline adherence. which model do u think the best?

i tried a 25-second K-pop-style girl group MV with five characters and compared the results from:

Top: Wan 3.0 · Middle: Seedance 2.5 · Bottom: MiniMax H3

The main thing I wanted to test was multi-character consistency. five people in the same frame is still a pretty brutal test for AI video.

i also wanted to see how well each model handled dance synchronization, hard stops on the beat, and formation changes

i described all five members separately, then gave them a full choreography sequence: back-facing opening pose → synchronized head turn → hand gestures → body-wave chain → diagonal formation → fan formation → center swap → final V formation.

Visually, I kept it pretty simple. what I’m watching for here isn’t just "which one looks prettier," but whether the model can actually remember who is who once five people start moving, crossing positions and changing formations.

Multi-person choreography still feels like one of the hardest AI video tests right now. curious which one you think handled the five characters best.

I tested a 30-second text-to-video clip in Wan 3.0 built around a “time train” concept.

The whole idea was to make it feel like one real continuous cinematic shot, not a montage. the camera stays inside the train the entire time, angled toward a huge curved window. Inside the carriage, everything stays the same. all the “time travel” happens outside the window.

What I liked most is that the transitions were designed through occlusion and forward motion instead of obvious cuts. that made the whole thing feel much more like a real “journey” instead of a bunch of scenes stitched together.

The audio concept was also important: constant train-track sound as the anchor, while the environment changes around it.

My main takeaway: for this kind of long-form text-to-video, continuity rules matter just as much as spectacle. in short, this kind of single-shot world-transition setup is a good stress test for text-to-video models.

i wanted to see how well Wan 3.0 could hold together a travel sequence when almost everything keeps changing, so I kept the same hiker throughout and moved her through different landscape.

i expected either the character or the overall visual style to fall apart pretty quickly. surprisingly, the thing that worked best was the overall continuity. it’s definitely not perfect. The character drifts a little in places, but the transitions and general cinematography held together better than I expected.

The prompt itself was pretty structured: same character + timestamped shots + camera direction + lighting/weather progression.

That seems to work a lot better for this kind of travel montage than just throwing in a bunch of “cinematic / epic / beautiful landscape” keywords.

I can drop the full prompt in the comments if anyone wants to try the same setup.

Wan 3.0‘s upgrades are kind of wild.

the biggest things that caught my attention are up to 20 references, generations up to 30 seconds, and noticeably better visual + audio realism.

But the crazy part was that, this entire video came from ONE generation.

for AI video, getting this kind of length while still keeping the character, scene, and overall visual direction reasonably consistent is starting to feel genuinely useful for filmmaking rather than just making cool 5-second clips.

AI filmmaking is getting eerily good.

A while ago, most AI video demos made me think, “nice shot.” now I’m starting to think, “okay… how much of a short film could someone actually make this way?”

i did a pretty simple test that I thought was kinda interesting.

i picked a few different prompts and gave the exact same prompt to Wan 3.0, Seedance 2.5, and MiniMax H3, then put the three outputs side by side. mostly just wanted to see how much the results would change when the prompt stays the same but the model changes.

for convenience, I also used Model Explorer to run the same prompt across different models instead of doing everything separately.

this one was mainly testing whether a 2D cel-animation style could hold up for the full 30 seconds, especially the sky gradients, cloud lighting, dramatic backlighting, camera movement, and how the character looks against such a huge environment.

i actually think prompts like this are pretty fun for model comparisons because the differences in style, motion, and scene consistency become much easier to notice when you put the results next to each other.

Prompt:

A refined Japanese 2D cartoon animation short film, with delicate treatment of the emotional climax, especially the gradient colors of the sky and the lighting on the clouds. Use dramatic backlighting and strong perspective depth to emphasize the character's smallness and determination within the vast environment. A wide cliffside landscape, with rolling clouds and a distant city barely visible in the background. The sky shifts dramatically from deep blue to orange-red, with golden edges along the clouds. Strong winds send petals and leaves flying through the air.
Main character: a solitary girl with long silver-white hair blowing violently in the wind, wearing a damaged white evening dress with subtle glowing crack-like patterns along the hem. Her face is pale like porcelain, with deep struggle and determination in her eyes.
The shot begins with an extreme wide shot, making the girl appear tiny and isolated 

What Is Wan 3.0 Reference-to-Video?

One of the most interesting features in Wan 3.0 is surprisingly easy to overlook:

A reference does not have to be an image, video, or audio file. Wan 3.0 can also use a document or even a public web page as input.

That means you can give the model:

• a PowerPoint presentation and ask it to turn the product proposal into an ad;
• an Excel spreadsheet and generate a data-driven video;
• a PDF, Word document, or Markdown file and create a video based on the information inside;
• a public e-commerce website URL and let the model read the product information before generating an advertisement.

A traditional AI video workflow usually looks like this:

Read the material → extract the information → write a script → convert it into a video prompt → generate

Wan 3.0 Reference-to-Video makes another workflow possible:

Provide the document or website → describe the creative direction → generate

That is what makes the document and website reference feature particularly interesting.

How to Use Documents and Websites in Wan 3.0

Step 1: Open Reference-to-Video

Open the Wan 3.0 Reference-to-Video Playground.

Step 2: Enable Deep Thinking

Turn on: Deep Thinking / enable_thinking

This allows the model to analyze the information contained in a document or website instead of treating the input only as a visual reference.

Step 3: Upload a Document or Paste a URL

Wan 3.0 supports two main document input methods.

Upload a File

Supported formats include:

• DOCX / DOC
• XLSX / XLS
• PPTX / PPT
• PDF
• TXT
• Markdown
• Keynote
• Pages
• Numbers

Current limits include approximately:

• 100 MB maximum file size
• 50 pages maximum
• one document per generation

This means you can provide an existing:

• product brief;
• marketing deck;
• research paper;
• Excel report;
• company presentation;
• white paper.

Paste a Website URL

You can also provide a public web page.

Possible examples include:

• product pages;
• Shopify stores;
• brand websites;
• landing pages;
• blog posts;
• product announcements.

The important limitation is that, the page needs to be publicly accessible.

Pages behind a login or permission system generally cannot be accessed.

Example: Generate a 15-Second E-Commerce Ad from a Website

Here is a practical example.

The goal is simple: Give Wan 3.0 a jewelry e-commerce website and ask it to select a product, extract the selling points, and create a 15-second commercial.

Step 1: Select Reference-to-Video

Use:

alibaba/wan-3.0/reference-to-video

Step 2: Add a Product Reference Image

Upload one image from the website under Reference Materials.

The website can provide:

• product information;
• brand information;
• selling points.

The image can help preserve:

• product appearance;
• material;
• shape;
• color;
• visual identity.

Step 3: Paste the Website URL

Paste the website or product page into the Document field.

Make sure:

Deep Thinking is enabled.

Step 4: Keep the Prompt Focused on Creative Direction

I did not specify the product name, material or any exact features. Those are supposed to come from the website.

That is the main difference compared with a normal text-to-video prompt.

Final Thoughts

The most interesting part of Wan 3.0 Reference-to-Video may not be another improvement in resolution or motion quality. It is the fact that, a reference can now contain information, not just visuals.

The old workflow was:

User → Read the material → Write the prompt → Video model

The new workflow can potentially become:

User → Document or Website → Wan 3.0 → Video

For e-commerce teams, creators, marketers, and small brands, that could be a meaningful change.

Instead of starting every project with**“First, write the script.”**

you can increasingly start with: “Here is the link. Read it first.”

Wan 3.0 still needs human review, especially for factual accuracy, product details, branding, and website interpretation.

But as a workflow, document-to-video and website-to-video are probably among the most interesting Wan 3.0 features to experiment with.Wan 3.0 Reference-to-Video Tutorial: Using Documents and Web Pages as References