Turn Any Topic into Viral AI Videos Using Google’s VEO3 model

I've been hearing a lot about these new AI video models that look incredibly realistic lately. People are actually creating entire TikTok channels based on them, and they look surprisingly good! I never really tried them myself, so I wanted to check them out. I decided to start with Google's VEO3 model that everyone seems to be talking about.

Instead of manually creating prompts and videos one by one, I developed an AI automation that will take any topic you have and turn it into banger videos. This way, I could quickly test the model's capabilities and see if it lives up to the hype.

So in this tutorial, I'll walk you through how I built this automation to:

• Generate creative video ideas from a simple topic input

• Craft optimized prompts for Google VEO3

• Produce high-quality AI videos with just a few lines of code!

Let's get started! 🚀

🔥 Get Code from Github now!

🤖 What is Google VEO3?

VEO3 is Google's latest text-to-video AI model that's been creating waves across the internet. Released just a few days ago, it represents a giant leap forward in AI-generated video quality. The videos it produces look shockingly realistic—to the point where they're often indistinguishable from actual footage at first glance.

What makes VEO3 special compared to earlier models?

• Realistic human characters with natural expressions, movements, and speech patterns

• Consistent environments that maintain physical properties throughout the video

• Proper lighting and physics that create a believable scene

• High-resolution output with smooth motion and transitions

The current limitation is that videos are limited to 8 seconds in length, but even within that constraint, the results are impressive. You've probably seen some VEO3-generated videos on social media already — those viral clips feature crazy realistic characters like the Yeti vlogger look almost too good to be AI-generated.

Don’t take my word for it — check it out yourself: 👉 @yetivloglife on TikTok

YEP!, that’s an AI-generated Yeti running a vlog, with over 356K followers and videos reaching 16 million+ views.

⚙️ How It Works?

1️⃣ Generate Video Ideas

The first step in our automation pipeline is to generate creative video ideas from a simple topic input. Instead of manually brainstorming concepts, I created an AI agent that handles this heavy lifting.

It takes a topic (like "Alien food critic reviewing Earth cuisine") and transforms it into structured, creative video concepts, complete with a catchy caption and environmental context.

GENERATE_IDEAS_PROMPT = """
You are an AI designed to generate 1 immersive, realistic idea based on a user-provided topic. Your output must be formatted as a JSON array (single line) and follow all the rules below exactly.

## RULES:

- Only return 1 idea at a time.
- The user will provide a key topic (e.g. "urban farming," "arctic survival," "street food in Vietnam").

### The Idea must:
- Be under 13 words.
- Describe an interesting and viral-worthy moment, action, or event related to the provided topic.
- Can be as surreal as you can get, doesn't have to be real-world!
- Involves a character.

...
"""

async def generate_video_ideas(topic: str, count: int = 1):
    print(f"Generating ideas for topic: '{topic}'...")
    user_message = f"Generate {count} creative video ideas about: {topic}"

    # Use the AI invocation function with structured output
    result = await ainvoke_llm(
        model="gpt-4.1-mini",
        system_prompt=GENERATE_IDEAS_PROMPT,
        user_message=user_message,
        response_format=IdeasList,
        temperature=0.7
    )
    return result.ideas

The LLM returns these ideas in a structured format using Pydantic models, which makes it easy to process the results

For example, when I input the topic "Alien food critic reviewing Earth cuisine" it might generate an idea like:

Idea: "Tentacled alien grimaces tasting hot sauce for first time"
Environment: "Retro diner, neon lights, zoomed close-up, documentary style"
Caption: "Alien reviewer tries Earth's spiciest sauce! 👽 #alien #foodcritic #spicyfood #tastetest"

This approach gives us a continuous stream of fresh, creative ideas that would be perfect for viral short-form videos.

2️⃣ Generate VEO3 Video Prompt

Once we have a creative idea, we need to transform it into a specialized prompt that works well with VEO3. This isn't as simple as passing the raw idea to the model — VEO3 performs best with highly detailed, structured prompts that follow certain patterns.

That's why I created another AI agent specifically designed to craft optimized VEO3 prompts:

async def generate_veo3_video_prompt(idea: str, environment: str):
    user_message = f"""
    Create a V3 prompt for this idea: {idea}
    Environment context: {environment}
    """

    # Use the AI invocation function
    result = await ainvoke_llm(
        model="gpt-4.1-mini",
        system_prompt=GENERATE_VIDEO_SCRIPT_PROMPT,
        user_message=user_message,
        temperature=0.7
    )
    return result

The GENERATE_VIDEO_SCRIPT_PROMPT contains detailed instructions for creating cinematic, hyper-realistic video prompts. Here's a snippet of what it tells the AI:

## REQUIRED STRUCTURE (FILL IN THE BRACKETS BELOW):

[Scene paragraph prompt here]

- **Main character:** [description of character]
- **They say:** [insert one line of dialogue, fits the scene and mood].
- **They** [describe a physical action or subtle camera movement, e.g. pans the camera, shifts position, glances around].
- **Time of Day:** [day / night / dusk / etc.]
- **Lens:** [describe lens]
- **Audio:** (implied) [ambient sounds, e.g. lion growls, wind, distant traffic, birdsong]
- **Background:** [brief restatement of what is visible behind them]

The prompt engineering is quite specific and follows patterns that work well with VEO3. For example, it instructs the AI to create selfie-style framing, include just one character (never named), specify a single line of dialogue, and describe physical actions and camera movements.

3️⃣ Video Generation with fal.ai

For the actual video generation, there are multiple providers out there, but in this tutorial, I'm using fal.ai, which offers pay-as-you-go access to the VEO3 model. This is perfect for testing without committing to Google's expensive subscription.

The fal.ai API generates videos in a three-step process:

• 📤 Submit a request including the AI model and video prompt

• ⏳ Wait for the video to be generated (which can take several minutes)

• 🔗 Retrieve the result with the video URL

Their Python SDK (fal_client) makes this process straightforward:

def start_video_generation(prompt: str):
    try:
        # Prepare the arguments for VEO3
        arguments = {
            "prompt": prompt,
            "aspect_ratio": "16:9",  # Can be "16:9", "9:16", or "1:1"
            "duration": "8s",
            "enhance_prompt": True,
            "generate_audio": True
        }

        handler = fal_client.submit(
            FALAI_MODEL,
            arguments=arguments
        )

        result = {"request_id": handler.request_id, "status": "submitted"}
        print(f"Successfully submitted to FAL. Request ID: {result['request_id']}")
        return result

    except Exception as e:
        print(f"Error submitting to FAL: {str(e)}")
        return {"error": str(e), "status": "failed"}

After submitting the request, we receive a request id and we need to wait for the video to be generated. VEO3 can take several minutes to render a video, so I created a wait function that periodically checks the status:

def wait_for_v3_completion(request_id, timeout_minutes):
    start_time = time.time()
    timeout_seconds = timeout_minutes * 60
    check_interval = 15  # Check every 15 seconds

    print(f"Waiting for video generation to complete (timeout: {timeout_minutes} minutes)...")

    while time.time() - start_time < timeout_seconds:
        status = get_video_status(request_id)

        if status.get("status") == "completed":
            # If completed, get the result with the video URL
            result = get_video_result(request_id)
            return result

        elif "error" in status:
            print(f"Error during video generation: {status['error']}")
            return status

        # If still in progress, wait before checking again
        time.sleep(check_interval)
        print("Still waiting for video generation...")

    return {"error": "Timeout waiting for video generation", "status": "timeout"}

Finally, once the video is generated, we retrieve the final result with the video URL:

def get_video_result(request_id):
    try:
        # Get the final result using fal-client
        result = fal_client.result(FALAI_MODEL, request_id)

        return {
            "status": "completed",
            "video_url": result["video"]["url"]
        }

    except Exception as e:
        print(f"Error getting video result: {str(e)}")
        return {"error": str(e), "status": "failed"}

One nice thing about the fal.ai API is that it returns a direct URL to the generated video, which can be viewed in any browser or embedded in a website.

4️⃣ Save Generated Videos

To keep track of all the videos we generate, I used a simple Excel sheet. It includes the video ideas, captions, prompts, and of course, the video links. This makes it easy to review everything later and share the videos with others.

Now that we've explained how everything works, I'm sure you want to test it out and generate your own crazy ideas 🤯! But first, let's talk quickly about the cost 💰 of using the VEO3 model.

💰 Cost of Using VEO3

The VEO3 model is quite impressive and delivers insane outputs, but all that quality comes with a hefty price tag 💸.

To use it directly with a Google account, you'd need to pay a steep $200/month subscription fee 😬.

As I mentioned, fal.ai offers a pay-per-use model, which is better for quick testing — but it's still pricey at $0.75 per second of video. So a typical 8-second short will cost you around $6 😳.

For me, that's a lot for just an 8-second clip, but for testing and having fun with the creative potential, it's worth giving it a shot! I hope these prices will drop over time as the tech improves and competition ramps up.

🚀 Try It Out

If you want to try this out for yourself, here's how to get started:

1- Clone the repository from 🌐 GitHub

2- Install the required dependencies:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

3- Create a .env file and add your API keys:

First, create an account on fal.ai. You’ll need to add at least $10 in credits to start testing video generation. Once that’s done, generate your API key from your fal.ai dashboard.

For AI models, I usually use OpenRouter to access various LLMs in one place — but you can use any other provider like OpenAI, Claude, or DeepSeek, since Langchain supports them all.

Now, create a .env file and add your API keys:

FAL_KEY=your_fal_ai_key_here
OPENROUTER_API_KEY=your_openrouter_key_here  # For LLM access

4- Finally, update the main script with your topic of interest and set the number of videos you want to generate (start with 1 to test things out):

async def main():
    # Change this to whatever topic you'd like to explore
    topic = "Dog playing piano in a jazz club"  # Your creative topic here
    await run_workflow(topic, count=1)

5- Run the script:

python main.py

The script will generate ideas, create prompts, submit them to VEO3, and wait for the videos to be generated. All the outputs, including the video URLs, will be saved to the Excel file (videos.xlsx). You can simply click on these URLs to view your generated videos in any web browser!

▶️ Here’s an example I got for “Alien comedian roasting humans for trusting AI” — generated entirely through the automation: 📽️ Watch the video

🔧 Improvements

I found this AI video generation field really fascinating and plan to continue exploring it. Here are some improvements I'm considering for future versions:

• Finding other AI models that might be better (and cheaper!) than VEO3

• Adding a way to directly upload the generated videos to YouTube or TikTok

And who knows — maybe I’ll even launch my own viral channel with some crazy, original concept like the Yeti vlogger, but with my own twist! 🔥😄

🌎 Use Cases

The output from VEO3 model is really impressive and will be huge for many applications:

• Content creation: Generate videos for social media, websites, and presentations

• Marketing materials: Create promotional videos and advertisements

• Educational content: Produce instructional and explanatory videos

• Prototyping: Rapid video concept development and testing

• Creative projects: Artistic and experimental video generation

• Business presentations: Professional video content for meetings and pitches

Although we need to wait for better pricing to see wider adoption, the potential is enormous. Early adopters who master these tools now will have a significant advantage as they become more accessible.

🎯 Conclusion

Google’s VEO3 is a big step forward in AI video generation 🎥. The videos it creates look incredibly realistic and open up exciting possibilities for content creators, marketers, educators, and more.

Right now, the pricing is a bit steep for casual use 💸 — but the tech is moving fast, and we’ll likely see more affordable options soon.

The automation I shared makes it easy to try out VEO3 without committing to expensive subscriptions. It handles everything — from idea generation to prompt creation to video production — all in one smooth workflow ⚙️.

Have you tried VEO3 or any other AI video tools? 💬 What kind of videos would you create with this tech? Let me know in the comments!

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