Here's How I Built an Anime Using AI (Step-by-Step)
Establishing rigorous multi-angle character sheets and environment reference stills before video generation is essential for maintaining asset consistency in narrative AI animation.
What this lesson covers
Caleb walks through a complete step-by-step pipeline for producing an anime sci-fi chase sequence set in Positano using AI tools. He demonstrates how to build multi-angle character sheets and matching environment reference stills to maintain character and visual consistency.
Key takeaways from the creator
AI-extracted notes, not independently verified product claims. Timestamp links let you check each point in the original video.
- 01:06 ↗
Designing anime concepts based on personal experiences and real-world location prompts enhances narrative resonance.
- 03:44 ↗
Using style references from image generation platforms establishes a baseline aesthetic for full project workflows.
- 05:18 ↗
Generating multi-angle character sheets with front, side, and facial close-up views establishes character continuity for downstream video tools.
- 07:48 ↗
Creating environment reference images with locked color grading and style inputs ensures visual consistency across sequence locations.
Workflow outlined in the video
- Gather visual style and character inspiration from reference galleries to lock in the target anime aesthetic.
- Draft a script and scene outline focused on key characters and distinct sequence locations.
- Generate multi-angle character sheets displaying front, side, and close-up views using image models like GPT-2.
- Render environment reference stills matching the established style and aspect ratio for each setting.
- Import character and location reference assets into video generation tools to generate consistent shots.
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
34,869 views captured 22 October 2026. ReelStack recommendation score: 55/100. Formula: radar-v5. This is ReelStack’s calculation, not a YouTube rating or a measure of factual accuracy.
Show the calculation
- 45% performance: 2.27× views vs 6 other channel videos observed at a similar age. View-evidence factor 97% (views / (views + 1,000))
- 25% freshness: 0/100 with a 14-day half-life
- 20% momentum: 175 views/day vs 4 channel baseline (5 comparable uploads), with the same view-evidence factor
- 10% engagement: 57/100; likes + 4× comments, smoothed with a 500-view neutral prior