How to Actually Make 100% Consistent AI Characters
Character consistency remains one of the biggest bottlenecks in AI filmmaking, and newer multi-angle training approaches offer a viable workaround to traditional reference image hunting.
What this lesson covers
Youri van Hofwegen demonstrates how to create and maintain consistent AI characters using OpenArt's updated character system. He walks through building characters from text prompts or multiple reference images, making them interact in a single scene, and generating high-resolution AI video.
Key takeaways from the creator
AI-extracted notes, not independently verified product claims. Timestamp links let you check each point in the original video.
- 01:55 ↗
OpenArt allows users to create consistent characters either by uploading one to four reference images or by using a text description with a selected model style.
- 03:12 ↗
The system generates four views—front, close-up, full body, and back—to train the character model for multi-angle consistency.
- 04:31 ↗
Users can combine multiple saved characters within the creation interface and reference them in prompts using tag handles like at-symbol names.
Workflow outlined in the video
- Navigate to the character section in OpenArt and choose to start with an image or a text description.
- Upload or generate multiple angle views (front, close-up, full body, back) to train your character effectively.
- Add a detailed backstory and name your character to give the AI better contextual grounding.
- Go to the generation interface, add multiple characters using tags, and input your scene prompt alongside reference images for positioning and clothing.
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.
How to interpret the numbers
96,360 views captured 25 October 2026. ReelStack recommendation score: 38/100. Formula: radar-v5. This is ReelStack’s calculation, not a YouTube rating or a measure of factual accuracy.
Show the calculation
- 45% performance: 1.30× views vs 3 other channel videos observed at a similar age. View-evidence factor 99% (views / (views + 1,000))
- 25% freshness: 0/100 with a 14-day half-life
- 20% momentum: 163 views/day vs 94 channel baseline (3 comparable uploads), with the same view-evidence factor
- 10% engagement: 1/100; likes + 4× comments, smoothed with a 500-view neutral prior