In this comprehensive guide, I'll walk you through the step-by-step process of creating automated faceless videos using powerful AI automation tools including Make.com, ChatGPT, and ElevenLabs. Discover how to build a complete video automation workflow for generating animated scenes, AI voiceovers, and auto-generated captions to create engaging content at scale. This no-code automation process is structured using Make.com and Airtable integration, enabling you to efficiently manage and customize your automated content creation pipeline at every stage of video production.
Understanding the Workflow
The automated workflow begins with configuring a custom Airtable database, where I've designed a structured data management system with four interconnected tables to organize your content: stories, scenes, scene segments, and API-generated multimedia assets. This dynamic database structure provides maximum flexibility for content automation, allowing you to define multiple content categories - from engaging storytelling narratives and data-driven news stories to futuristic scripts. Each content type can be paired with customized AI prompts through seamless API integration, making it effortless to modify video styles and content without disrupting your established automation workflows in Make.com.
Setting Up Airtable
The first step involves setting up an Airtable base to manage the stories and scenes. I created a main table called “Stories,” where each row represents a new story. This table is linked to a "Story Types" table that defines different orders of stories, similar to ridiculous news or print-realistic news. Each story type is associated with unique prompts that will be used later to generate content. Also, each story is broken down into multiple scenes, which are managed in a separate "Scenes" table linked to the main "Stories" table.
Step 1 : To Create the Necessary Fields in Airtable
Story Types Table Define different story types and associate them with specific prompts.
Stories Table Use fields for story ID (bus-incrementing), source textbook, and generated story.
Scenes Table Each scene is linked to a story and includes fields for narrative textbook and AI image prompts.
Video API Table This table tracks the images and videos generated through the Leonardo.ai API and associates them with the corresponding scenes.
Step 2 : Generating Story and Scene Content
Once the Airtable setup is complete, the next step is to automate the generation of stories and scenes using ChatGPT. This is done by driving automation in Make.com that sends a request to ChatGPT with the source format and named story type.
A new row is created in the “Stories” table, and the automation triggers ChatGPT to generate a narrative grounded on the source formate and the chosen story type.
Scene breakdown The generated story is also broken down into scenes. Each scene is further divided into multiple corridors, which will be used to generate individual images or amped videos.
This process ensures that each story is stoutly created and grounded on the input content, making it largely customizable for different types of video systems.
Step 3 : Creating Images and Videos
With the scenes generated, the next phase involves creating visual content for each part of the scene. This is done using the Leonardo.ai API, which can generate both still images and amped videos from the handed AI prompts. Still image generation For each part of a scene, the automation sends a request to Leonardo.ai to generate a still image. These images can be used as stationary examples in the video. animated video generation alternately, Leonardo.ai can generate animated videos from still images. This adds a dynamic element to the example, making the final video more engaging.
The generated images and videos are saved in the “video API” table, where the automation monitors their progress until completion. formerly all corridors of a scene have been reused, the scene is marked as complete, and the workflow moves to the next phase.
Step 4 : Assembling the Video
With all scenes completed, the next step is to assemble them into a full video. This involves combining the images or amped videos into a correct sequence and adding voiceovers and captions.
Combining Scenes: The automation combines the still images or amped videos for each scene using the JSON to video tool. This tool also allows for the application of simple goods, similar to zooming in on the images to add stir.
Voiceover Generation: Using ElevenLabs, the automation generates voiceovers for the narrative of each scene. The voiceover lines are also synced with the video scenes to create a flawless audio-visual experience.
Incorporating Audio and Video: The final step in this phase is to combine the voiceovers with the corresponding video scenes. The automation handles this process, ensuring that each video scene is paired with its corresponding audio file.
Once the video scenes are intermingled with the audio, the scenes are sutured together into a full-length video. The automation also adds captions to the video, furnishing a complete, polished product ready for distribution.
Step 5 : Customizing and Spanning the Workflow
A major benefit of this automation setup is its rigidity. You can fluently customize the workflow to create different types of videos by simply changing the story type or modifying the prompts used to generate the content.
Customizing Story Types: By adding new story types to the “Story Types” table, you can create different video formats, such as educational videos, promotional content, or news summaries.
Conforming Image and Video Parameters: The automation allows you to acclimate the parameters used for image and video generation, similar to resolution, aspect rate, and vitality styles. This makes it easy to conform the final product to specific conditions or preferences.
Spanning Up: The entire workflow is designed to be scalable, allowing you to automate the product of multiple videos contemporaneously. This is particularly useful for content generators who need to create large volumes of video content efficiently.
Final Studies and Next Step
This end-to-end automation solution for creating faceless video content using Make.com, ChatGPT, and ElevenLabs demonstrates the transformative potential of integrated AI tools and no-code automation workflows. Through intelligent process automation, you can optimize your video production pipeline, eliminate manual bottlenecks, and scale content creation efficiently while maintaining high production quality.
Whether you're a digital content creator seeking to streamline video production or a business innovator exploring AI-powered content automation solutions, this scalable workflow framework provides a robust foundation for success. The customizable nature of this automation system, combined with its scalability features, opens unlimited possibilities for creating engaging, AI-generated video content at scale.
For professionals ready to implement this automated content creation system, I recommend beginning with a basic Airtable-Make.com integration setup, then progressively expanding your automation pipeline as you master these powerful business automation tools. Exploring the various APIs and AI integration possibilities outlined in this guide will unlock additional opportunities for workflow optimization and process improvement.
By implementing these automated video production strategies, you'll be equipped to create professional faceless videos through an efficient, AI-powered content generation system that delivers consistent engagement for your target audience.
Ready to start your AI automation journey? Contact us to learn how we can help implement these solutions for your business, or explore our other guides on business process automation and digital transformation. Get in Touch with Expert: www.growwstacks.com Learn Automation Yourself. Join our Learning Community (Free for 7 days): https://www.skool.com/automation-diy
Comments