Over the past few weeks, I’ve been building Veo3.im — an AI-powered text-to-video generation tool. The concept is simple: you write a natural-language prompt, and the system returns a short video based on it. Sounds cool, right?

But the reality of building it wasn’t so simple.

In this post, I’ll walk you through the biggest technical challenges I faced, how I approached solving them, and what I learned in the process. If you’re interested in generative AI, prompt engineering, or real-time video generation — I hope this helps.

🎯 The Goal

Let users type something like:

“A girl in a red dress walking through a futuristic city at night”

…and get a 5–10 second video that actually matches that prompt — in visual quality, in structure, and ideally, in motion logic too.

The goal wasn’t just to “generate video,” but to generate something people might actually want to use in content creation or storytelling.

🧱 Challenge 1: Low-Resolution Outputs (and Why 512p Isn’t Enough)

Most video generation models I tested defaulted to 512p or 768p — barely usable beyond prototypes. The footage looked fuzzy and lacked detail, especially on human faces and fine objects.

✅ What worked:

I split the video generation process into scene chunks, allowing fine-grained control over resolution and frame detail.
Instead of post-generation upscaling (which created artifacts), I implemented native 1080p rendering at the frame level, combining it with guided interpolation to avoid flickering between frames.

This drastically improved the output quality — now the visuals look crisp enough for actual usage in Shorts or Reels.

🧠 Challenge 2: Natural Language ≠ Structured Video Logic

A lot of prompts look great on paper:

“A young man in a black suit walking into a neon-lit bar.”

But for models, that kind of sentence is ambiguous. Should the bar be crowded or empty? Is it indoors or outdoors? Is the camera tracking or static?

💡 My fix: Prompt Decomposition Engine

I built a lightweight NLP module that takes the raw prompt and breaks it into structured elements like:

subject_attributes: clothing, age, gender
scene_context: location, lighting, time of day
motion_instructions: walk/stand/run, direction, camera behavior

This made the prompt far more usable for downstream processing, especially in combining it with a diffusion-based scene builder.

💸 User Onboarding Insight: Why I Introduced a $9.9 Trial

Many users liked the idea of AI-generated video, but were hesitant to pay without seeing real results.

So I launched a $9.9, 3-day trial that allows unlimited prompt submissions and full-HD exports. This helped lower the friction while giving me valuable feedback on common prompt types and edge cases.

🔧 Still a Work-in-Progress

Like any indie project, it’s far from perfect. Here are a few things I’m still working on:

Smoother motion transitions: especially when scenes shift between zoom, pan, or rotate.
Audio sync: background music doesn’t always align with motion pacing.
Scene continuity: ensuring multi-shot videos feel cohesive, not stitched together.

🔍 Under the Hood (Tools & Stack)

Model base: Diffusion-based video models with custom embeddings
Inference engine: OpenVINO for faster frame generation
Backend: Python + FastAPI
Frontend: React + Tailwind + FFmpeg orchestration

Everything is designed to be modular and iterated quickly. I'm planning to open-source the prompt parser module soon, especially if there’s interest from the community.

🧵 What’s Next?

If you’re building in the generative video space, experimenting with prompt engineering, or just curious about how these tools work — I’d love to hear your thoughts.

👉 Try it here: https://veo3.im

Got suggestions? Want to collaborate? Or just curious about how the pipeline works?
Drop a comment — I’d be happy to connect with other Hashnode devs working on similar challenges.

Let’s keep exploring what’s possible with AI + storytelling. 🎥

🚀 From Prompt to Video: Lessons from Building Veo3.im, a Text-to-Video Generator with Real-World Use in Mind