How to Make Scarily Real AI UGC Videos
As AI video tools gain mainstream adoption, mastering structured character consistency and prompt engineering is essential for producing reliable commercial content without endless retries.
What this lesson covers
This video breaks down a professional workflow for creating hyperrealistic AI user-generated content (UGC) ads using Arcads. It demonstrates how to structure character creation, reference image conditioning, and video generation to maintain stability and control.
Key takeaways from the creator
AI-extracted notes, not independently verified product claims. Timestamp links let you check each point in the original video.
- 01:26 ↗
Arcads provides access to multiple underlying video generators like Kling and Google VEO, allowing users to select high-end models for specific ad projects.
- 02:33 ↗
Building a custom character starts with finding a reference image persona and using an LLM to generate a detailed descriptive prompt including film stocks and camera settings.
- 03:51 ↗
Editing and iterating on character images before animation is crucial for establishing high-quality reference frames that ensure visual consistency in the final video.
- 04:43 ↗
Integrating product reference images into the character generation phase allows the AI to render products with accurate 3D placement and interaction.
Workflow outlined in the video
- Select a reliable AI UGC platform like Arcads to manage character and video pipelines.
- Gather reference imagery from platforms like Pinterest to define the visual style and persona of your AI character.
- Use an LLM like ChatGPT or Gemini alongside your reference image to write a detailed prompt incorporating camera settings and film styles.
- Generate and upscale base character images using high-end image models before moving to animation.
- Feed your finalized character and product reference images into an advanced video model like Google VEO or Kling to produce the final ad sequence.
Before you use this workflow
These notes describe the source video at its publication date. Model access, pricing, connectors and interfaces may have changed. Check the original source and the provider’s current documentation before installing an add-on, connecting an account or spending credits.
ReelStack has not independently tested this workflow. Preview one representative shot and check motion, continuity and output quality before applying it to a full production. No result, cost saving or model capability is guaranteed.
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How to interpret the numbers
19,421 views captured 25 October 2026. ReelStack recommendation score: 21/100. Formula: radar-v5. This is ReelStack’s calculation, not a YouTube rating or a measure of factual accuracy.
Show the calculation
- 45% performance: 0.46× views vs 15 other channel videos observed at a similar age. View-evidence factor 95% (views / (views + 1,000))
- 25% freshness: 0/100 with a 14-day half-life
- 20% momentum: 7 views/day vs 13 channel baseline (7 comparable uploads), with the same view-evidence factor
- 10% engagement: 4/100; likes + 4× comments, smoothed with a 500-view neutral prior