Seedance 2.5 Tutorial: Cinematic AI Video From Start To Finish
Provides practical guidance on capturing authentic cinematography data (cameras, lenses, and color grades) from live-action references to maintain visual continuity across AI image and video models.
What this lesson covers
Rourke Heath demonstrates an end-to-end workflow for creating cinematic AI video projects using Seedance 2.5. He covers reference gathering via ShotDeck, prompt generation with Claude, and Midjourney image creation as the foundation for consistent video generation.
Key takeaways from the creator
AI-extracted notes, not independently verified product claims. Timestamp links let you check each point in the original video.
- 01:37 ↗
ShotDeck provides technical camera, lens, film stock, and color hex data from cinematic frames that can be fed into Claude to establish consistent visual parameters.
- 02:47 ↗
Maintaining consistent virtual camera bodies, lenses, and depth-of-field instructions across prompt generations is essential for continuity between shots.
- 04:45 ↗
Evaluating reference frames like a social media feed helps identify high-contrast lighting and compositions that naturally capture viewer attention.
- 07:36 ↗
Reference images can be loaded into Midjourney as both image prompts and style references to replicate framing and aesthetic qualities.
- 07:58 ↗
Claude can analyze an uploaded reference frame to generate tailored Midjourney prompts matching its lighting, lens characteristics, and composition.
Workflow outlined in the video
- Browse reference platforms like ShotDeck to locate frames matching the desired mood, lighting, and color grading.
- Extract camera gear, lens focal length, lighting setups, and color palette data from reference frames.
- Upload reference stills into Claude to generate detailed Midjourney prompts incorporating technical camera specs.
- Upload the reference frame into Midjourney as an image prompt and style reference to generate initial visual assets.
- Use the generated character and location reference assets to drive Seedance 2.5 video generation.
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
22,966 views captured 26 September 2026. ReelStack recommendation score: 61/100. Formula: radar-v5. This is ReelStack’s calculation, not a YouTube rating or a measure of factual accuracy.
Show the calculation
- 65% performance: 1.00× current views vs the channel's recent median; same-age history is not yet available. View-evidence factor 96% (views / (views + 1,000))
- 25% freshness: 96/100 with a 14-day half-life
- Momentum pending: collecting daily snapshots; its 20% weight goes to observed performance, not free points
- 10% engagement: 58/100; likes + 4× comments, smoothed with a 500-view neutral prior