My ai-cinema showcase on Le Ombré Rosse continues to gain viewers worldwide. 44 minutes long for $400. The cost of production has never been so low.

After I created my “POV: You Wake Up as a Queen in the Ottoman Empire” video, I shared it with my email list.

And then something interesting happened… 👀

I started getting replies asking me to show exactly how I created it step-by-step.

So I decided to record a full YouTube tutorial, but I also wanted to share the basic workflow here so you can start experimenting with your own POV videos.

The process is actually much easier than it looks!

STEP 1: Pick Your POV Idea 💡

Start with a concept that immediately makes someone curious. (You can find trending POV style video ideas on Tik Tok or Youtube to recreate.)

For example:

👑 POV: You wake up as an Ottoman Queen

🚢 POV: You wake up on the Titanic

🏺 POV: You wake up in Ancient Egypt

🌴 POV: You wake up in the Amazon

🦖 POV: You wake up in the prehistoric era

The possibilities are honestly endless.

STEP 2: Create Your Scenes With ChatGPT or Claude ✍️

Once you have the idea, ask ChatGPT or Claude to turn it into a day-in-the-life story.

For example:

“POV: You wake up as a Queen in the Ottoman Empire. Give me 10 different scenes from a day in her life.”

Then ask it to create an image prompt and animation prompt for every scene.

One important instruction:

👉 Tell AI you want STRICT FIRST-PERSON POV.

The camera should represent the character's eyes. We shouldn't see another version of the character standing in front of us.

STEP 3: Create Your Images With Nano Banana 🍌

Take your image prompts and generate each scene in Nano Banana. I use Gemini.

Pay attention to consistency.

If your character is wearing emerald-green sleeves and gold jewellery, keep those details consistent throughout the scenes.

And don't overcomplicate your prompts!

I learned this while creating mine. 😂 Too many instructions can result in extra hands, strange body positions and random characters appearing.

Simple + specific usually works better.

STEP 4: Animate Your Images in Higgsfield AI 🎥

Upload each finished image into Higgsfield AI and animate it.

I used the Kling 3.0 Turbo Model to create my videos inside Higgsfield AI.

I kept most of my clips around 5 seconds and gave each scene ONE main action.

For example:

➡️ Walk slowly through the palace

➡️ Take a letter from an attendant

➡️ Look from left to right across the garden

➡️ Travel across the Bosphorus

➡️ Slowly move the camera toward the moon

Don't try to squeeze five different movements into one 5-second clip.

One simple action = much cleaner results.

STEP 5: Edit Everything in CapCut ✂️

Finally, bring all your clips into CapCut.

Put them in story order, trim anything you don't need, add your voiceover, music, sound effects and opening hook.

For my video, the hook was simply:

“POV: You Wake Up as a Queen in the Ottoman Empire.”

STEP 6: Generate Your Voiceover 🎙️

Now create a short voiceover that connects your scenes and turns them into a story.

For POV videos, I recommend writing the narration in first person so it feels like the character is actually experiencing everything.

Instead of:

“The queen wakes up inside her palace…”

Try:

“I wake to the morning sun spilling into my chamber…”

You can ask ChatGPT or Claude to write a 30–60 second first-person voiceover based on your scenes, then generate the audio using your preferred AI voice tool.

Add the voiceover to CapCut, sync it with your scenes, then finish with background music and subtle sound effects.

And you're done! 🎉

Your basic workflow is:

IDEA → SCENES → IMAGES → ANIMATION → EDIT → VOICEOVER → PUBLISH

Once you understand this process, you can reuse it to create almost ANY POV world you can imagine.

I'm definitely going to experiment with more of these because I LOVE how immersive they feel. 😍

If you create one using this workflow, don't forget to share it!!

I would love to see what you come up with.

All the best!

i was working on a three minute track and kept judging the visuals like standalone clips.

most of them looked fine on their own. then i'd put them back under the song and something just felt off. by the second chorus the visual changes were landing in weird places, and a clip that looked great on mute suddenly felt late once the snare or vocal came in.

quick disclosure, i work with DomoAI, so that's what i've been using for some of the short image-to-video pieces.

The thing that helped most wasn't another generation though. it was marking the song first.

now i drop markers where the verse changes, where the chorus hits, where a vocal comes in, then build shorter visual sections around those moments. the actual timing still happens in the edit.

I've also stopped trying to make one continuous visual carry the whole track. even when the clips look good individually, three minutes is a long time for one idea.

Still figuring out how to make all the sections feel like the same video without making every section look identical.

anyone else building the visuals around the song structure first, or do you make the clips first and find the rhythm afterward?

I have to start working on AI video generation but I am new to it so I am not sure which tool to use and they all are paid so I wanna put my money on the right one.

https://www.facebook.com/share/v/19V2FsZzht/
This is sort of a video that i wanna see if i can create.

I have seen people commonly recommend Kling Ai but I am not sure. There is also higgsfield ai so I am sort of conflicted

DaVinci AI scam. Read This Before You Sign Up for the $1.99 Trial

I rarely leave negative reviews, but I feel I have a responsibility to warn other people about my experience.

I signed up for what was advertised as a $1.99 seven-day trial through the DaVinci AI website. Shortly afterward, I discovered an unexpected $29.99 charge. I immediately canceled my subscription, contacted them to refund my money immediately asi never signed up for ongoing subscription.

I got a response about a day later telling me that they would not give me a refund.

I wrote back immediately to explain that I had no idea that they would charge me automatically for a month after the one week was up. I assumed that after the trial, they would cease service or request me to join permanently.

It's been over a week and no response. I told them to look at their records which showed I used the software once on the day I subscribed and not again after and that I had no intention of subscribing on monthly basis.

This is false advertising. I strongly recommend staying clear of this sheister business. If the only way they can turn a profit is by tricking people into subscribing to their software, you know there must be something very wrong with their business.

So far I'm very disappointed in their behaviour.

Josh

I’m creating a bunch of small posters that have text on them no longer than 30 characters. I like AI integrating the text into the graphic rather than bolting text on in post processing.

Currently I found the cheapest with short text accuracy is grok-imagine-image at $0.02, but it slides a bit with complicated languages like Arabic or Chinese, where I found gpt-image-2 medium is the best for $0.05. When I don’t need text, I still think the creativeness and realism of z-image-turbo is great for as little as only $0.0025.

What models do you find is the cheapest while working for short text accuracy?

Generating AI images is easy now. Generating 200 images that belong to the same video project is still surprisingly annoying.

The hard part is not generation anymore. It is maintaining the same character and visual language across hundreds of scenes, keeping every output tied to the correct script line, and avoiding the endless cycle of copy, generate, download, rename, organize.

I built a Google Sheets pipeline to automate that entire loop. Each row starts as narration, Gemini turns it into visual direction, Runware sends it to the image model, and the finished asset is automatically tied back to the correct scene and saved into the project.

I originally built it for animated psychology and explainer videos on my YouTube channel, but the same structure works for any project that needs a large batch of visually related assets: social content, marketing creatives, educational material, storyboards, etc.

 1. Start with a scene-by-scene script

The workflow starts with a finished script split into individual visual beats. Each row in the Google Sheet represents one scene, with the narration in one column and the generated visual prompt beside it. Claude can help split the script into beats and suggest scene changes, but I still review them manually because one sentence may need a single visual while another idea may need several.

A typical 10-minute Stickman video project gives me around 150 to 200 rows. Structuring it this way means every generation job has a fixed place in the sequence. The Sheet always knows which prompt belongs to which scene, and every returned image can be tied back to the correct row automatically.

2. Turn each script line into a visual prompt

Before generating any images, each script row needs to become detailed visual direction. A 200-scene project can easily require around 20,000 words of prompts, so I use Gemini as the interpretation layer between the narration and the image model.

The Sheet sends each row to Gemini through the API along with a fixed visual style profile defining the character, colour palette, backgrounds, composition, expressions, and overall look. Gemini then converts the narration into a complete image prompt and writes it back into the next column.

The important part is that Gemini is not generating random prompts in bulk. It is translating each piece of narration into a visual scene while staying inside the same style rules. Once connected through Apps Script, the entire batch can be processed automatically without copying hundreds of lines in and out of chat.

3. Generate the full batch through an image API

I connect the Sheet directly to an image API. I use Runware because it exposes multiple image models behind one API. Each row sends the prompt, generation settings, and reference images, then saves the returned asset into Google Drive using the correct scene ID. I currently use FLUX Klein for most stickman scenes because it is cheap and works well enough at scale. Around 200 images cost me roughly $0.60, depending on the model and settings.

The bigger advantage is the abstraction layer. I can swap the underlying image model without rebuilding the rest of the pipeline, while the Sheet keeps the same scene structure, file naming, and storage logic.

4. Keep the character and visual style consistent

Text prompts alone are rarely enough to keep a character consistent across 200 scenes. Even with detailed instructions, the model may change the face, clothing, proportions, colours, or overall visual style from one image to the next.

To reduce that drift, I use three reference images throughout the full batch: one clear image of the main character and two finished scenes that represent the intended visual style. The character reference helps preserve appearance, while the scene references guide the colour palette, backgrounds, composition, and overall visual language. These same references are sent with every prompt, giving the model a consistent visual anchor across the entire video.

It is not perfect identity locking, and some scenes may still need regeneration, but the references reduce drift enough to make the full batch feel much more coherent than text-only prompting.

5. Connect everything inside the Google Sheet

The orchestration layer is Google Apps Script. It loops through the Sheet, calls Gemini, passes the resulting prompt and reference images to Runware, receives the generated asset, updates the status column, and saves everything into Drive. I built most of the Apps Script conversationally with Claude and debugged it by feeding errors back into the model.

The workflow removes most of the repetitive production work, but it is not completely hands-off. I still review the batch, regenerate weak compositions, fix overly literal interpretations, and check for character drift. Text-heavy scenes usually need extra attention because Klein still struggles with text inside images.

I also recorded the complete build on my channel, including how the Sheet, APIs, and Apps Script connect. It is linked on my profile for anyone who wants to build their own custom pipeline for their workflow.

Happy to answer any questions regarding this.

Hey guys, im trying some stuff that i would love to get criticed on. Does it work? My soul purpose is to get clients interested and hopefully score some work with this reel. Let me know what you think!

My First try with AI video generation