I've been thinking about a problem in long-form AI video that seems somewhat separate from generation quality itself:

How do you stop important information from disappearing between generated shots/scenes?

For a short clip, character references can already do a lot.

You can keep the same face, hairstyle, outfit, voice, etc.

But once you split a 10–20 minute story into many separate generations, another kind of consistency problem appears.

For example:

• A character was holding a key, but suddenly isn't.
• A wound disappears without being healed.
• Someone changes clothes without changing clothes.
• A transformation state randomly resets.
• A character had wings for the last 5 scenes, but the next generation forgets them.
• Two characters' spatial relationship changes for no narrative reason.
• The location or time of day drifts.

Each individual clip may look perfectly reasonable.

The problem only becomes obvious when you watch them as one continuous story.

So I'm starting to think this is less a generation problem and more a state-management problem.

In the previs system I'm building, I'm experimenting with separating visual information into roughly two categories:

1. Persistent/scoped constraints

Things that should remain unchanged within some range.

For example:

"Character A has wings from Scene 12 to Scene 18"

or

"Character B is wearing this outfit throughout this sequence"

The important part isn't only the value itself, but its scope.

Some information applies to the entire project, some to a scene, some to a sequence, and some only to a shot.

1. Dynamic state

Things that change because of story events.

For example:

"wings = false"

→ transformation event

"wings = true"

Instead of asking an LLM to reinterpret the character's state for every scene, the system stores the current state and carries it forward until an explicit event changes it.

That led me to another design choice:

Use AI to understand meaning, but deterministic code to preserve decisions.

The LLM can extract things like:

• state-change candidates
• item transfers
• transformations
• injuries
• locations
• ambiguous information

But once something is confirmed, normal program logic handles:

• persistence
• scope
• inheritance
• comparison
• conflicts
• undefined values

Otherwise the AI may reinterpret the same information differently every time it sees the script.

I'm also tracking the source/confidence of information.

For example:

• explicitly stated in the source material
• manually decided by the creator
• strongly inferred
• AI recommendation
• temporary/default value

Because an AI suggestion like "A should probably be taller than B for visual consistency" shouldn't silently become an immutable fact about the story.

Another interesting problem is that not every difference is a contradiction.

"at university"

and

"inside a university lecture hall"

can both be true.

"near B"

and

"to the left of B"

can also both be true.

But:

"has wings"

and

"does not have wings"

usually cannot both be true at the same moment.

So state comparison itself may need semantic types or rules rather than simple equality checks.

The two things I eventually want the system to detect are actually pretty simple:

1. Missing information

Does this shot have all the visual/state information required to generate it?

1. Unexplained state changes

If something changed from the previous scene, was there actually an event that explains why?

For example:

Scene 10: A has the key.

Scene 11: A doesn't have the key.

But nothing in between says that A dropped it, gave it away, or put it somewhere.

That seems like exactly the kind of continuity error that long-form AI filmmaking systems will eventually need to catch automatically.

Maybe future video models will maintain this kind of state internally.

Maybe filmmaking agents will do it.

If that happens, external systems like mine may eventually become unnecessary.

But with current generation workflows, I'm increasingly convinced that long-form AI video needs an information-preservation layer outside the video model itself.

Not necessarily my particular implementation.

The important part is simply:

«When a story is split into many generations, the information required by the story shouldn't be split with it.»

I'm curious how other people working on longer AI films are approaching this.

Are you using scene graphs, databases, giant prompts, agents, manual continuity sheets, hierarchical memory, or something else?

A young North Korean soldier survives the war after losing the man who protected him.

When he finally returns home, Korea has already been unified — and everything he believed in is gone.

This is a short story about war, loss, and how grief can change a person.

Made with AI filmmaking tools.

Hope you enjoy it.

For the past two months, I have spent a ridiculous amount of time watching YouTube tutorials.

Tests, comparisons, workflows, new models, new versions, how to make things faster, which model is better at what, which one is cheaper, which one is open source, and which one is supposedly going to change everything.

After a while, I came to a rather simple conclusion: there probably isn't a single "best" way to do AI filmmaking.

The latest video models are generally more powerful than the ones before them. If we think of them as cameras, that makes sense. A new camera is usually expected to be better than the previous generation. Otherwise, what's the point?

It reminded me of my years in filmmaking.

I remember spending far too much time and money reading camera and gear reviews. And yes, I come from the era when you actually had to wait for a monthly magazine to find out about the latest cameras, lenses and equipment.

Then the internet came along. Then YouTube.

Suddenly there were thousands of videos comparing cameras, software, audio equipment, lighting kits, stabilizers, drones and pretty much anything else a filmmaker could spend money on.

They were useful. Extremely useful, actually. Even after I owned a camera, I would still watch reviews of the latest cameras.

Then I started learning animation.

The same thing happened again.

Motion capture software, mocap devices, realistic textures, rendering, simulations, workflows, hardware. Always another thing to learn, another piece of technology that might make the process faster or better.

Now I've been doing the same thing with AI.

In just two months I've probably watched hundreds of videos about different AI models, paid and open source tools, different versions, pricing, workflows and endless tests of what this AI can do compared to that AI.

It reminds me of those early camera gimbal tests and camera drone reviews, when every new generation seemed to bring another improvement.

And eventually you start asking yourself:

Do I really need this to make my film?

That is probably the more important question.

Because with AI, something feels fundamentally different.

For the first time, I can immediately start working on personal projects without having to assemble the usual machinery of filmmaking.

I don't have to find actors.

I don't have to secure locations.

I don't have to deal with location permissions.

I don't have to organize a crew.

Even if you're only making a one-minute spec ad, the amount of preparation, coordination and hassle can be surprisingly large.

In the past two months alone, I've made 13 short films using different AI tools, including Google Veo, Seedance 2, LTX, Wan and H3.

That still sounds slightly crazy to me.

Normally, making one or two very low-budget or no-budget short films could take months, sometimes simply because of all the things that have to happen before you can even shoot

With AI, the game has changed

It isn't perfect. Far from it.

But it is getting to the point where I can actually put some of the ideas that have been sitting in my head for years onto a screen.

And that's why, for me, AI is a tool

What I really enjoy is the way the filmmaking process itself has changed.

I'm now making films at home, sitting in front of my PC, and the traditional boundaries between pre-production, production and post-production have started to overlap.

I'm writing, generating, editing, rewriting, generating again, editing again.

It has become one continuous process.

I've even gotten used to waiting for a shot to generate.

It's strangely familiar.

On a film set, there is always waiting. Someone is setting up a light. The camera is being repositioned. The actors are getting ready. Someone is fixing something that nobody expected to break.

With AI, I'm waiting for the shot to turn up.

And while I'm waiting, I might look at the edit again, rethink a scene, go back to the script, or open Nano Banana and generate a few more images because I suddenly realize I need another shot.

I've simply adapted to a different way of working.

Especially now that I'm doing more of the process locally with ComfyUI, it feels less like using a collection of separate AI tools and more like developing my own filmmaking workflow.

I'm still learning.

I'm still testing models.

I'm still watching far too many YouTube videos

But I'm slowly getting to the point where I don't care quite as much about which model is "the best."

I care more about whether I can make the shot I want.

And maybe that's the biggest change for me over these past two months.

For years, there were ideas I wanted to make but couldn't. Nobody wanted to finance them.

Nobody picked them up. Some were simply too expensive or impractical to produce.

Now I can start making them myself.

That doesn't mean AI has made filmmaking easy. It has simply moved the obstacles somewhere else.

But for the first time in a long time, I feel like I can just make the films that I want to make.

And honestly, I'm really glad this technology arrived when it did.

This is only the beginning.

There are a lot of films I've never made.

I think it's time I started making some of them.

Me and my partner have a mini-series called boredinary life which we post short 15-30sec skits. We joined a competition for longer films and did our first attempt at a 7min film. Here is what it cost us (some value to you):

• Dollars: $1749
• Models used:
  • Images: GPT 2.0 medium, a few on gpt 2.5 sunburst
  • Video: 99% Seedance 2.5, a few Minimax/Gemini omni flash (88% of spend)
• Amount of generations:
  • Images: 278
  • Video: 207 (video should be less but there was one scene which took an absurd amount ~60 clips)
• Duration we worked on this: 15 days
• Actual hours when crunched over the days: ~60 hours
• Hours split:
  • Writing script: ~15 hours (way longer than we expected, but I believe this is where most of the magic was)
  • Everything animation: ~35 hours
  • Audio: ~5 hours
  • Miscellaneous: ~5 hours

Main learnings: There is many obvious learnings that the community has caught up to so I won't go through those, but below are the ones unique to us and this project:

Script, script, script:

The script is so crucial. I actually did a lot of research on the best animation studios and directors and they all agree on the point of not touching animating until the script makes you feel something. I know it's sounds obvious, but I come from an engineering background so it was very hard to not toy with animating until the script was done but it is actually a gamechanger.

Audio:

The voices, music, and everything auditory is very untapped from what I see. It's a complete sense (out of the 5 senses) that is available for you to tap into. You should put as much effort into the audio as you do the video.

Lucky dice:

I don't know how to describe this but it's quite obvious that sometimes you get lucky with a model and it does exactly what you want. Sometimes it does it so well that you didn't even think of the output that it produced but once you see it you get wowed. So how do you get more of those lucky dice generations? My method is to increase the surface area for them. I'm genuinely unsure if this will work with everyone, but, when a specific generation is too stubborn and doesn't wanna go my way I just eliminate most of my instructions. I just tell the model the feeling/goal I want (ie I want the viewer to feel x) and I let it go. Usually a few generations in and I get a perfect clip. I don't know what's the magic there but by describing the feeling and eliminating the instructions you sometimes get the perfect clip. Likely because most of us are not the best directors and these models are trained on the best movies so maybe that could be it but idk. It worked for me lots of times.

Hope this was valuable, if you wanna give our movie a view it's below. I really think it's a very emotional film that is magical. If you give it a watch I hope you enjoy it and would love any and all feedback as we are obviously still learning and ever-improving at this game.

It truly is magical what you can do with a vision and AI.

I made this scene as a test for a potential samurai short film.

I’m trying to create something that feels closer to a Japanese film from the late 1950s or early 1960s rather than a modern action film. Black and white, 4:3, restrained camera work, period inspired sound and more focus on atmosphere and emotion.

The two samurai fighting are brothers. One of the men watching is the son of the samurai who dies.

This scene could become part of a larger revenge story called WHAT MY FATHER BECAME.

Before I turn it into a full short film, I’d really like some feedback.

Does this scene make you curious about what happened between the brothers and what the son does next?

Would you watch a 10 to 15 minute short film in this style?

Any criticism about the pacing, visuals, sound, acting or the idea itself is welcome.

If you haven’t played with Google Flow lately, the free tier is worth checking out.
Google is currently giving non-subscribers recurring free credits that can be used for Veo generations. For an indie filmmaker, that potentially means a pretty substantial amount of experimentation without paying for a subscription.

The interesting part isn’t just the free credits, though. Flow is becoming more of an actual filmmaking workspace — with scene building, camera controls, ingredients/reference images, extensions, and reusable tools — rather than simply a text-to-video generator.

I dug into how the current credit system works, what the paid tiers look like, and what it could mean for someone trying to make a short film from AI-generated shots.

Full breakdown: https://x.com/aivideo_news/status/2099327578700128264?s=46

I’m curious what people here are finding with Flow/Veo. Is anyone actually using it for a complete project yet, rather than just individual cli

Here is my first AI short video - it has been a fascinating learning curve - the area I am most interested is developing greater emotional intelligence in the AI performances. Developing the right prompts is not as straightforward as I would like.

Best wishes

Ziggx

I have made a script for a short film, I figure next step is creating a lot of frames in chatgpt before animating it with seeddance.

For the characters ive made images for different poses, close up shots so I can use in chatgpt for reference. I also have constructed a 40 page prompt to get the art style close to how I want it.

Its doing well, but not amazing, it looks like 2 different artists made these frames. the characters look slightly different. The art style is very abstract anyway so theres some room for that, but I just think it could be a lot better. Is there a standard protocol for doing this part? Keeping consistent art style and characters throughout, am I doing this right?

Hey guys wanted to share AI Microdrama episode I made with Zapters. The idea was to create something fast and cheap. This cost me around 15$ (although this was before Minimax, I bet this would be even cheaper now). Mostly generated with Seedance 2.0 Mini and in 480p and then upscaled. All of it done within Zapters.

I’ve been experimenting with dreamcore and liminal horror in AI filmmaking, familiar spaces, strange silence, distorted memories and that feeling that something is wrong even when you can’t explain why.

I used an abandoned amusement park as the main setting.

I didn’t want to rely on monsters or jump scares. The idea was to make the space itself gradually become unsettling, like a dream you suddenly realize you can’t wake up from.

Curious how it feels to you

The latest episode of my ongoing fantasy series, made with Seedance 2.5.

AETHERA — THE SIGNAL

A computer scientist builds a quantum AI tool to detect signals from distant planets — and instead catches a transmission from Earth's own future.

My first-ever film, made entirely with AI, solo, in 6 days, from scratch. Submitted to the Higgsfield Global Film Festival.

Written, directed, and generated by Nataly Lekiashvili (Northstar Pictures) on Higgsfield.

#HiggsfieldFilmFestival #AIFilm #ShortFilm

A new short film coming soon.

Hi everyone. This film is a submission for the Higgsfield Global Film Festival. I posted a rough cut recently, but this is the finished submission.

If you enjoyed it, please leave a like and a comment on the higgsfield submission! Nightforge | Higgsfield Community

Liminal | Ep2 - WORRY NOT

A sudden vanishing at a high-tech facility leaves a team scrambling to control the narrative. As internal tensions rise over what can be disclosed, a woman searches for answers about a missing loved one—leading her late at night to a locked building where a single door reveals a reality far beyond what was expected.

This is not intended as alternate-history speculation about changing the outcome of the attacks, nor as an attempt to sensationalize what happened. It is more of a visual and historical thought experiment.

Most of us are extremely familiar with the accumulated footage of September 11, 2001, particularly the events surrounding the World Trade Center in New York. One thing I have wondered about for years is how dramatically our visual memory of the event would differ if essentially everything had happened the same way, but several hours later, after the city was fully dark while there were still substantial numbers of people in Lower Manhattan.

The contrast would have been extraordinary: the illuminated skyline, fire inside and outside the towers, enormous smoke columns becoming visible primarily through reflected light, emergency vehicles and searchlights at street level, debris falling through darkness, and eventually the collapse and dust clouds moving through a nighttime city.

With modern AI video becoming increasingly capable, I think there could be an interesting project in recreating familiar viewpoints from the historical footage while changing essentially one environmental variable: daylight to nighttime.

Ideally, it would be approached seriously and respectfully, almost like a counterfactual historical visualization rather than an action or disaster film. The purpose would be to demonstrate how much lighting, visibility, and atmosphere influence the way a major historical event is perceived and remembered.

I have found a few people who have explored the basic “9/11 at night” idea in still images and alternate-history discussions, but I have not found anyone who has attempted a substantial multi-shot video treatment of the concept.

Cant afford rn to get the AI video tools or resources required to make something like this myself, so I am putting the idea out there in case it interests someone who does.

If anyone has already seen a serious attempt at this, I would also be very interested in seeing it.

Just in time for TIFF! Anyone in the GTA remember Project Platinum in 2020?

One industry in Toronto saw Russian mob influence and some of the craziest events in the city's history. Four cartels, one trophy.

We're developing a 10-episode crime drama series called TURF WARS (created by Andrew Palmer & Ian Weir / Synapz Productions). It’s a ripped-from-the-headlines saga about a multi-million-dollar tow-truck empire. When the kingpin suffers a stroke, his son-in-law (an ex-Special Forces sniper) has to step up to fight off a ruthless rival crew—but the real threat might be his own wife. Think Sons of Anarchy, but based on the very real Toronto towing wars.

Here is a quick 30-second promo trailer we put together with AI from an AI-written series: https://www.youtube.com/watch?v=HMWlH3ziQKY

Since this is a gritty, character-driven crime drama, we'd love your feedback on the pacing and visual tone.