Higgsfield AI Tutorial for Beginners | Make Your First AI Video
Ideal for beginner AI filmmakers seeking a structured image-to-video workflow that locks in character design before generating motion shots.
What this lesson covers
Diego Galvao demonstrates how to create a complete AI video project using Higgsfield AI. He walks through generating a base character, building a multi-angle character sheet for consistency, and outputting final video scenes with Seedance 2.5.
Key takeaways from the creator
AI-extracted notes, not independently verified product claims. Timestamp links let you check each point in the original video.
- 0:56 ↗
Generate initial character concepts in Higgsfield by tweaking descriptive base templates to establish lighting, subject framing, and environment.
- 2:58 ↗
Create a 16:9 three-panel character sheet from a selected base render to maintain front, back, and profile consistency across future generations.
- 6:36 ↗
Drag the generated character sheet into Higgsfield's video generation interface as an active reference before outputting motion clips.
- 7:25 ↗
Structure multi-shot video prompts by combining LLMs like ChatGPT or Claude with modular prompt frameworks detailing camera movement, lighting, and action.
Workflow outlined in the video
- Select an image model within Higgsfield and enter a detailed character prompt to generate base concept renders.
- Choose the strongest character render and drag it into the reference window to generate a 16:9 three-panel character sheet.
- Switch to the Higgsfield video generation tab and set the character sheet as your visual reference.
- Draft a sequential video prompt specifying shot breakdown, character actions, camera movements, and location details.
- Submit the video prompt alongside the character reference to render consistent video clips with Seedance 2.5.
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
218 views captured 9 November 2026. ReelStack recommendation score: 32/100. Formula: radar-v5. This is ReelStack’s calculation, not a YouTube rating or a measure of factual accuracy.
Show the calculation
- 45% performance: 0.73× views vs 11 other channel videos observed at a similar age. View-evidence factor 18% (views / (views + 1,000))
- 25% freshness: 88/100 with a 14-day half-life
- 20% momentum: 48 views/day vs 78 channel baseline (11 comparable uploads), with the same view-evidence factor
- 10% engagement: 53/100; likes + 4× comments, smoothed with a 500-view neutral prior