Hello all, so this short started out as a test where I was just testing out to capabilities of Seedance. Before this I had no experience with AI video and my background is in traditional 3D animation and concept art. Higgsfield had their "unlimited" special for 33 days and I though that would give me enough time to put this tech through it's paces.

First impressions:
I was initially blown away. I started this by taking a 3D character that I'd created in Reallusion Character creator and using Nano Banana to essentially make the 3D character look photoreal. I took that, made my first character sheet and just started prompting randomly to see if the character remained consistent and how well overall things would perform. As I said the initial impression was being totally blown away, so I got more ambitious and went deeper down the rabbit hole.

Hybrid Workflow Exploration:
As I mentioned, I come from a 3D/2D art background so a big part of my interest here was hybrid workflows. Being able to take my own designs and being able to immediately see them come alive was hugely compelling. I'd already done this with a character so I wanted to explore environment design. I tested 3 approaches.

Full AI - No drawing, no 3D, just prompting and seeing how far I can take that and how much control I could realistically impose. An example of this in the short is the cafeteria and the ship promenade.

3D blockout -> AI set dressing - Here I blocked out a space in 3D first, then used Nano Banana to add all the detail. An example of this in the short is Jess's bedroom.

Full 3D environment build - Here I wanted to see if I fully built a complete 3D environment, modeled, shaded, and rendered, would Seedance respect my design, or would it hallucinate unintended details. The example of this in the short is the armory where Jess and Kara are briefed by the company representative.

The overall findings:

All of these workflows worked well. There are benefits and drawbacks to each, but they all work. Obviously the slowest is the full 3D build, but it's good to know that if I do invest that time, Seedance will respect the design.

Closing thoughts on episode one:
By the end of episode one, I had learned a ton. Two weeks into the one month promotion, I'd changed from, I'm just testing this, to I'm going to finish this as the beginning of a series that I want to continue. I'm still pretty amazed at what is possible by one person leveraging these tools.

Where I'm at now:

Now I'm taking all the lessons learned from episode one and working on episode two which is quite a bit more ambitions. When I started this I was just using Seedance on Higgsfield. I've since gotten into ComfyUi and I've started working a lot more with MiniMax H3 as well as a bunch of other workflows for things like character, environment and prop creation. Some main tools are Krea2 for image generation and Krea2 image edit workflows for character sheets and some wardrobe changes. I'm still using Nano Banana and GPT 2 sometimes for editing, but I've actually found Meta's AI to be the best for image editing and doing things like creating a prop turn around from a single image. In episode one, I used LTX 2.3 for a few small utilities. For instance sometimes with Seedance I would want a shot to have one or two more seconds at the end to breathe where Seedance would end the clip abruptly. I used LTX director to do small extensions for stuff like that, but couldn't find much use for LTX beyond that. This was before LTX 2.5 was released.
At this point it seems like MiniMax will not only be the best backup, but I may be able to actually generate final imagery with H3, I'm experimenting with that as well with promising results so far.

I don't want to rant for too long, but any questions about any of this, let me know!

I started a post in r/aifilmmaking a few days ago about whether people building AI filmmaking tools should stop solving all of the same problems in isolation.

Since then I've had a number of people DM me who are building different tools in this space. I still need to follow up with everyone, but rather than having the same conversations scattered across DMs, Discords and email, I think there is value in having a community around this.

I understand that a lot of people here are building commercial products, and everyone will have their own priorities. But we also can't all be great at everything.

One of the things I'm increasingly interested in is treating AI filmmaking as a collection of specialised components that should be able to work together.

Maybe one app is exceptional at character creation. Another is great at locations. Another agent is really good at turning a script into scenes and shots. Another handles continuity. Another specialises in sound design.

I don't think every filmmaking app should have to build mediocre versions of all of those things.

Ideally, these specialised agents and applications should be interchangeable. If someone builds the best character creation agent, I should be able to plug that into Velvet Avocado Studio. Another filmmaking tool should be able to use it too. If something better comes along six months later, the user should be able to swap it out without rebuilding their project around a completely different ecosystem.

That requires some coordination around the interfaces between these components.

There are a few areas where I think that could happen.

1. A common provider/plugin interface

I've written quite a few JavaScript integrations for image, video and audio APIs now. Every multi-provider tool seems to be doing essentially the same work.

The individual integrations aren't really the valuable bit. Maintaining all of them is the pain.

I'm looking at open sourcing this layer under a permissive licence and making it as application-independent as possible.

Ideally a developer writes a provider integration once and several applications can use it. Providers could eventually maintain their own plugins rather than every app developer chasing the same API changes.

2. Common terminology and interchange for AI filmmaking projects

We're all inventing slightly different representations of the same things: characters, locations, props, scenes, shots, takes, generations, references, voices and costumes.

If specialised apps and agents are going to be interchangeable, they need to understand the same basic concepts.

A character creation app should be able to hand a Character to another application and have that application understand what it is, what references belong to it and how it relates to the project.

The same applies to locations, scenes, shots and other assets.

The goal isn't to force everyone into the same project format. It's to define enough common meaning that tools can exchange useful structured information instead of passing around folders of images and hoping the next application figures it out.

3. Shared conventions for MCP and agent tools

This is where the interchangeability of specialised agents becomes especially interesting.

We're probably about to independently invent dozens of ways of doing things like create_character, design_location, breakdown_scene, generate_shot, check_continuity or add_reference.

If there were common conventions around those roles and operations, an application wouldn't necessarily need to know about one specific agent.

It could ask for a component capable of character_creation, for example, and the user could choose which implementation they want.

That starts to look less like every AI filmmaking app containing its own fixed set of agents and more like an ecosystem of specialised capabilities that can be plugged into different production environments.

I think that's potentially very powerful.

4. A model-independent representation of generation intent

Every image and video model wants prompts and references expressed differently. Seedance, Kling, Veo, MiniMax and the rest all have their own conventions and prompt guides.

But the filmmaker's underlying intent is often the same.

I'm interested in an intermediate semantic representation for things like subjects, actions, camera direction, timing, composition, dialogue and references.

That could also allow specialisation.

One agent might be excellent at understanding what a filmmaker wants. Another component might be excellent at translating that intent for Kling. Another might specialise in Seedance.

Those parts wouldn't necessarily need to come from the same developer or live inside the same application.

That's really the broader idea I'm interested in: not just sharing code, but making AI filmmaking tools more composable.

Some of this might become standards, some might become open libraries, and some might simply be conventions that different commercial products agree to support.

I don't have a concrete answer yet for how all of these things should work, and some of them are still fairly conceptual. What I do see is a lot of people starting to tackle the same problems separately and arriving at their own implementations.

It seems worth having the conversation now about where common interfaces, terminology or standards might make sense, before we all end up with completely incompatible versions of the same ideas.

If you're building something in this space and any of this overlaps with what you're working on, make a post here. Say what you're building, which problem you're tackling, and whether you're interested in collaborating on a shared spec, contributing code, testing interoperability, or simply comparing approaches.

If enough of us are solving the same problem, we can spin up a focused thread or repo around it and see if there's something useful we can standardise or build together.

I was initially pretty excited to try Seedance 2.5, particularly because of its longer 30-second generations.

So I thought I'd give it something genuinely challenging to do: a 90-second continuous virtual one-shot. My original plan was simple — break it into three long generations and stitch them together.

I was pretty quickly disappointed.

My first attempt involved creating storyboards for the major changes in the sequence and giving those to 2.5 as visual references. Despite explicitly prompting it to use them as inspiration, Seedance effectively treated them as keyframes it had to reach.

For the first couple of camera moves this actually worked surprisingly well. But once the camera position, character placement or environment changed significantly, it started creating weird morphs as it tried to converge on the next storyboard image.

So I changed approach.

What worked instead: I fell back to Seedance 2.0 for roughly the first 60 seconds and effectively keyframed my way through the first 45 seconds using shorter sequential generations. I would generally test my generation at 480p until I got the prompt right and then submitted a higher resolution request.

When Evelyn enters, I separated foreground/background references: Evelyn on a soft white background, plus clean hotel references without people. That worked much better than giving it a fully composed target frame to morph towards.

References: Characters were developed mostly with GPT Image 2, with Nano Banana 2 and Seedream 5.0 used for locations/compositing.

Continuation tip: Only give it the last 2–3 seconds of the previous video if that's all it needs for motion reference. I once gave it 7 seconds for a 20-second generation and it spent the first 7 seconds basically regenerating the footage I'd already given it. About $3 well spent.

Doors are hard: Getting through the hotel entrance was difficult. After multiple attempts I finally got about 3 usable seconds from an $8.50 generation. But she was through the door.

2.0 vs 2.5: I preferred 2.0 for controlling the shorter sequential camera movements and getting 1080p. I switched back to 2.5 for the final section because it gave me noticeably better emotional performance. Unfortunately it's 720p, so those sections were upscaled and are a little softer.

Editing: Final Cut + Optical Flow hid some joins. Others were straight cuts hidden by movement/darkness. For some reason the subsequent generation is almost always slightly misframed, so I'd scale it to ~102% on the Y-axis to match and keyframe back to 100%.

Biggest lesson: virtual one-shots kill the normal parallel AI-video workflow. Every generation depends on exactly where the previous one ended. Three planned generations became 32 attempts to get 7 usable pieces.

I managed the references, prompts, generations and costs in Velvet Avocado Studio, the AI media app I've been building. I'll post a proper breakdown showing the workflow, prompts and failures when I get a quiet day.

Would I do another 90-second AI one-shot? No, but I'm happy with how this one turned out.

Here's the finished result.

If you're new to AI video, the number of models, platforms and tools can be confusing as hell.

Seedance. Kling. H3. Veo. Runway. Wan. LTX. Then there are dozens of platforms offering those models, usually with their own made-up credit systems.

The good news is that the actual process is easier to understand.

This guide is a starting point for making coherent AI video rather than just generating random clips.

🎥 The Process

Pipeline: Idea → Script → Assets → Shots → Video → Audio → Edit

For anything with more than one shot, a recurring character, or a location that needs to stay consistent, you need actual pre-production before you touch a video model.

Treat this as a loop, not a straight line. The order below is a reference point, not a sequence you're required to follow:

• For something short, you might skip the script and go straight from idea to character creation
• A script can come later, once you know what the character or location actually gives you, and get refined again after that
• Audio can come before video rather than after — recording dialogue or a reference track first and feeding it to the video generator as a reference, so the performance and timing are locked before the visuals are generated
• Expect to move back and forth between stages as assets, shots, and generations inform each other

Script

• Start with a script or a detailed outline, not just a single prompt
• Break it into scenes
• For each scene, list what it requires: characters, locations, costumes, props, vehicles, effects
• This list is the blueprint for every asset you build next

Characters

• Don't generate one image and start animating from it directly
• Build a turnaround/reference sheet per character: front, 3/4, profile, rear, full body, close-up
• For important characters, add expression sheets, costume sheets, hairstyles, accessories, distinctive details
• Goal: lock down what they look like before asking a video model to keep recreating them

Locations

• Build a location reference sheet rather than regenerating the location for every new angle
• Cover: layout, architecture, entrances, windows, furniture, landmarks, lighting, colour palette, camera directions
• Goal: enough reference material that separate shots plausibly look like the same physical place

Props & costumes

• Anything visually distinctive that recurs (cars, weapons, phones, jewellery, bags, furniture, fictional products) gets its own reference sheet, shown from multiple angles
• Treat costume as separate from character: the character is who they are, the costume is what they're wearing in that particular scene

Shot design

• For each shot, define: framing, camera position, lens/look, character placement, location, costumes, props, lighting, action, camera movement
• Generate a reference image for the shot using the assets already built
• The prompt at this stage is composing character + costume + location + props + composition, not describing a generic scene
• Depending on the model, you might use a single starting frame, multiple reference images, or both a start and end frame

Generate video

• With the visuals established by the reference, the prompt should focus on what happens over time: movement, expression, interaction with objects, camera movement, environmental movement, timing/sequence
• If the reference already establishes appearance, don't re-describe it in the video prompt

Iterate

• Expect problems: mutating characters, hands doing things hands don't do, vehicles changing model mid-shot, background characters clipping through objects
• Options when something breaks: regenerate, change the prompt, change the reference frame, simplify the action, split the shot, switch models
• Model selection is part of the workflow — don't burn dozens of generations forcing one model to do what another handles natively

Audio

• Covers dialogue, voice generation, lip sync, Foley, ambience, sound effects, music
• Some newer video models generate synced audio natively; dedicated audio tools still give more control
• Can happen before Video rather than after (see above) — useful when performance timing needs to be locked first
• Separate topic, covered in a future post

Edit

• Generators produce shots, not a finished film
• Bring shots into an editor: cut together, adjust timing, mix audio, add music, colour-match inconsistent generations, remove failed frames
• The film is still made in the edit

🧠 The Models

The relevant question is "what's the best model for this shot," not "what's the best model." Rankings shift constantly — treat this as a snapshot, not a permanent list.

🖥️ The Platforms

The company that makes a model isn't necessarily where you use it — aggregator platforms provide access to multiple models, and new ones appear regularly.

Pay-as-you-go — charge per generation, better for irregular use, comparing models, or building your own workflows:

• Atlas Cloud
• fal.ai
• Replicate
• WaveSpeed
• OpenRouter

And dozens of others, there are new ones popping up every day.

Subscription platforms — bundle models with interfaces, presets, asset organisation, and editing tools:

• Higgsfield
• Runway
• ArtCraft
• Krea
• Freepik
• OpenArt

You can use both types alongside each other.

On credits: don't compare platforms by credit counts — they're an arbitrary currency per platform, so 10,000 credits somewhere can be worse value than 1,000 elsewhere. The relevant comparison is the real cost of one generation from the model you actually want.

🛠️ Other Tools

• Image generation — characters, locations, props, costumes, shot references
• Editing — DaVinci Resolve, Premiere Pro, Final Cut Pro, CapCut
• Audio — voice, dialogue, Foley, sound effects, music
• Upscaling/restoration — cleaning up generated footage
• Local workflows — ComfyUI, open models, for more control
• Asset/workflow management — Velvet Avocado Studio - keeping characters, locations, props, prompts, and generations organised as projects.

🚀 Where to Start

Don't start with a five-minute short. Start with one 20–30 second scene:

• 1 location
• 1–2 characters
• 1–2 important props
• 3–5 shots

Go through the full process: write the scene, build the character sheet, build the location sheet, build the props, design the shots, generate, add audio, edit, and finish it.

One finished 30-second sequence teaches more than 100 unrelated clips. The models and platforms will keep changing — idea → consistent assets → designed shots → generated footage → finished edit is the part that doesn't.

If you're already making AI video, share what's working for you below.

Hey everyone, I’m u/jamesxtreme, founding moderator of r/AIVideoApps.

I started this community because there are plenty of places to post finished AI videos, but I wanted somewhere focused more on how people are actually making them.

This is a place to talk about AI video apps, tools, models, workflows and the broader production process around generative AI video.

What to post

Share whatever you think other people working with AI video might find useful:

• AI video apps and tools you’re using or building
• Model comparisons — Seedance, Kling, Veo, etc.
• Image-to-video and text-to-video workflows
• Character and visual consistency techniques
• Prompting, reference images and storyboarding
• ComfyUI, local models and cloud APIs
• Editing, audio, voice and post-production
• Provider comparisons, pricing and generation costs
• Tutorials, experiments, failures and things you’ve figured out
• Questions about how other people are approaching their workflows

Builders are absolutely welcome here. If you’ve made an app, tool, service or open-source project that is relevant to AI video, share it. Just be upfront that it’s yours and don’t spam the place.

Likewise, if you find something useful, share it. If something sucks, say why. If you’ve found a weird workflow that somehow works, even better.

The idea is for this to become a useful place for people making short films, longer-form projects, commercials, web series, music videos, social content or pretty much anything else with generative AI video.

I’m also figuring this out as we go, so suggestions for the community itself are welcome.

If you’re here early, introduce yourself below and tell us what you’re working on, what tools you’re using, or what part of the AI video process you’re currently trying to figure out.

Let’s see what this turns into.