AI Music Video Production Toolkit
Plan characters and review shots for AI music videos
A reference image answers what a character looks like. A shot still needs a job: open the door, react to someone, cross the room. Then it has to fit the next shot.
Here is a review exercise to try before paying for another generation:
1. Write one sentence describing the action the shot must communicate.
2. Mark the usable start and end of the generated clip. Judge the movement, not just its best frame.
3. Watch that section beside the neighbouring shot. Check whether the character, direction and action still make sense.
4. Record a decision: keep, trim or regenerate, with a specific reason. A problem outside the usable section may only need a trim.
For example, a character might look right while standing still but change appearance during a turn. A shorter usable section may work in the edit; if the turn is the point of the shot, a trim does not solve the brief. This is an illustrative review exercise, not a claim that a particular tool can guarantee the result.
My own production cases include roughly 30 AI idol-video versions and seven musical-style PLANET ME videos. I keep the idol motion-review cases and musical character-planning cases separate in my AI Music Video Production Toolkit.
The English toolkit is USD $49: two visual guides totalling 49 pages, 16 silent study clips and 11 editable CSV worksheets. The clips are private study material, not stock footage or finished videos for reuse. No generation credits or software subscriptions are included.
Contents and previews: https://lucete.gumroad.com/l/check-in-shot-selection?utm_source=indiehackers&utm_medium=product_update&utm_campaign=shot_review_20260930
I run Lucete AI Lab and sell this toolkit. The production examples are my own; AI assisted the English editorial preparation.
When you review an AI-generated clip, what most often changes your decision after you put it next to another shot?
I tried to make a K-pop-style AI idol music video. I made about 30 versions, kept a selection of rough experiments and one stronger final edit, and posted some of the strange results as a numbered TikTok series.
The failures were entertaining, but they also left a practical question: how do you decide which generated shot is worth keeping? A good-looking still does not guarantee usable movement.
I later built seven musical-style PLANET ME music videos with recurring characters. That gave me a second set of cases: character planning and visual continuity across a series.
I have turned those two bodies of work into the AI Music Video Production Toolkit. This is a separate product from my DeckGuard founder tools.
The English download includes:
• Two PDF guides, 49 pages in total
• 16 short silent clips for studying individual shots
• 11 editable CSV worksheets for planning and review
It is for independent creators who already generate AI video but need a clearer way to plan characters and decide: keep, cut or regenerate. The idol experiments are the motion-review cases; the musical-style series provides character-planning cases.
Price: $49, one-time digital download.
See the contents and previews here:
https://lucete.gumroad.com/l/check-in-shot-selection
This is not a video generator, a library of complete music videos to republish, or a guarantee of perfect choreography. The short study clips are silent. Production observations are distinguished from newly prepared exercises; I am not claiming to provide a complete original prompt history.
Made by Haerang Yoo at Lucete AI Lab, using my original AI-assisted PLANET ME productions.
Which ruins more of your generated shots: character drift or movement?
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The 30-version process gives you strong evidence about your own bottlenecks, but the commercial question is whether those bottlenecks recur for other creators. Have any external users tried the toolkit yet, and did it change what they kept, cut, or regenerated?
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I don't have external before-and-after results to report yet. Those 30 versions are evidence from my own production process, so I wouldn't treat them as proof that the toolkit works for other creators.
The distinction I'd test is whether it changes a decision on someone else's footage. For example: does a creator spot a continuity problem before spending more credits, rescue a usable two-second section instead of discarding the whole clip, or simplify a shot that keeps failing?
I'd have them review a small batch first using their usual approach, then use the worksheets and record what changed and why. A different decision isn't automatically a better one; it still has to work in the edit.
That's the evidence the product needs next. For now, it's a case-based production resource with original examples, not a workflow I can claim has been validated across users.
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That operational-use signal is useful, especially the shift from single-person to multi-person shops. If you’re open to it, what’s the best email to reach you on?
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What did you have in mind: a question about the toolkit, or a potential project? A little context here would help before moving to email. Also, I haven't described a shift from single-person to multi-person shops in this thread, so I'm not sure which example you're referring to.
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Sorry — I mixed up your thread with another one. What I had in mind is a question about how you’re thinking about validating the toolkit with external creators. If you’re open to it, what’s the best email to reach you on?
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Thanks for clarifying. What specifically would you like to know about testing it with external creators? Feel free to ask here so the discussion is useful to other makers too. If you're proposing a collaboration, please share what you have in mind and your role first.
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