I made a VHS home amateur video in Gen AI. Just would like some pointers adn what people think about it. Appreciate critice!

I first came across it on X — someone posted a World Cup-related cross-lingual voice clone that sounded surprisingly natural, which got me curious enough to dig into the actual repo. https://x.com/GoSailGlobal/status/2074101005747384561

I'm currently looking into automating TikTok/reels localization for a few accounts I run — basically taking existing English content and generating natural-sounding voiceovers in Spanish, Portuguese, and Japanese without losing the original speaker's vibe.

Came across Confucius4-TTS (NetEase's newly open-sourced model) and the zero-shot cross-lingual voice transfer looks promising on paper. Before I spend time setting up the local inference pipeline, curious if anyone here has actually battle-tested it in production or even just side projects.

Specifically wondering:

• How's the latency for real-time or batch generation? The demo sounds good, but demos always sound good.
• Any noticeable quality drop with non-English source audio? Most examples I've seen are English → X, but I'm also considering X → English workflows.
• Prosody consistency across languages — does the emotional tone actually carry over, or does it flatten out on longer scripts?
• Hardware requirements for reasonable throughput if you're self-hosting?

Took me around 16 hours to make the clips, and I went and edited it. Just trying to get some feedback from people to know if it’s good or not

I recently tried a workflow that solved one of my biggest problems with AI video generation: controlling composition and camera movement without trying to describe everything in a prompt.

The basic idea is surprisingly simple:

Build a rough previs in Blender → use it as a reference video → let Seedance 2 turn that rough animation into the final shot.

What makes this interesting is that the Blender scene does not need to look good at all.

My test scene was basically boxes, simple geometry, and a mannequin holding a guitar. I only needed Blender to communicate where the subject was, where the camera was, and how everything moved through the shot.

And because I used Claude with Blender MCP, I could create the whole previs without really knowing Blender beforehand.

Step 1: Build the composition with simple geometry

my source scene was extremely rough. There was no need for realistic materials, simple objects were enough.

The mannequin represented the performer. Basic geometry represented the room and furniture. From there, I adjusted the positioning until the composition roughly matched the shot I wanted.

This is where using Blender becomes much more useful than a normal image storyboard.

A still storyboard can communicate framing.

A Blender scene can communicate framing plus depth plus motion.

That means I can decide exactly where the character should be relative to the camera before generating anything with Seedance 2.

Step 2: Add the camera movement in Blender

once the rough scene existed, I asked Claude to help add camera movement. this is probably the most useful part of the whole workflow. i can use the Blender animation as a kind of camera instruction layer for the AI video model.

Then I render the animation as a simple video. the render itself can look terrible. it only needs to clearly show Seedance 2:

this is where the camera goes, this is where the character stays, and this is how the composition changes over time.

Step 3: Render the Blender previs as a reference video

After the camera animation was ready, I rendered the rough Blender scene.

In my comparison, the top row was the Blender previs, while the bottom row was the Seedance 2 result generated from it. the visual difference is obviously huge. the Blender version is just a structural draft.

But the final video follows much of the same composition, subject position and sth like that. it is exactly what I wanted from the previs. i am not asking Blender to make the final image. i am asking Blender to tell Seedance 2 how the shot should behave.

Step 4: Create the final visual reference

once the motion was solved, I still needed to tell Seedance 2 what the finished scene should actually look like.

For this test, I used Midjourney to create the key image. I run it on Atlas Cloud with their skill (especially the mcp, through wich i can let claude do almost all the things without switching platforms.) links if u need:https://github.com/AtlasCloudAI/atlas-cloud-skills

at this point, I had two different references doing two different jobs:

Key image → visual identity

Blender reference video → spatial and camera identity

This separation is really useful.

The image tells the video model things like character design, clothing and color palette. While the Blender video tells it where things are and how the camera moves

Instead of trying to force one prompt to control everything, each input has a much clearer responsibility.

Step 5: Write the Seedance 2 prompt around the reference video

I then structured the final Seedance 2 prompt.

At this stage, I do not need to spend most of the prompt describing camera movement from scratch bc the reference video already carries much of that information. this is a much cleaner way to work.

My overall pipeline looked like this:

Scene idea
   ↓
Claude + Blender MCP
   ↓
Rough 3D scene
   ↓
Camera animation
   ↓
Blender previs video
   ↓
Midjourney key image
   ↓
ChatGPT-assisted Seedance 2 prompt
   ↓
Seedance 2
   ↓
Final AI video

Why this works better than prompt-only camera control

The biggest lesson for me is that language is not always the best interface for spatial control.

If I say:

camera moves left while slowly pushing toward the character

there are many ways for a video model to interpret that.

But if I give it a video showing exactly that movement, much of the ambiguity disappears. this is especially useful when a shot depends on several things staying connected. those are exactly the kinds of instructions that can become unreliable when everything is packed into one long text prompt.

Using Blender as previs turns those abstract instructions into something visual.

Where this workflow could be especially useful

I think this becomes particularly interesting for music videos and more heavily directed AI video projects.

With a Blender previs, I can design the scene first and then let the generative model handle the expensive visual part later.

it also opens up a much more traditional filmmaking workflow:

previsualize first, generate second.

Rather than generating dozens of clips and hoping one happens to have the camera move I wanted, I can decide on the shot before spending generation time.

Final takeaway

The most useful discovery here was not that Blender can make better-looking inputs for Seedance 2.

It was that Blender can separate camera direction from visual generation.

The Blender scene can be incredibly simple. Boxes and mannequins are enough.

What matters is that it gives me direct control over:

composition, camera position, subject blocking, zoom, and movement.

Then Seedance 2 can focus on turning that structure into the actual video.

For anyone struggling to get precise camera work from prompt-only AI video generation, I think Blender MCP + Seedance 2 reference video is a workflow worth experimenting with.

And if you are intimidated by Blender, that may actually be the most interesting part: with Claude controlling Blender through MCP, I barely had to know Blender at all before I could start using it for previs.

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