Bud Knows Ball
Bud is a grizzled silverback who vlogs about sport. Turning him into short-form video meant solving the problem every AI video project hits — the character looks different in every clip. A two-stage pipeline fixed the face first, then animated it.
- Client
- Bud Knows Ball
- Year
- 2026
- Built with
- HiggsfieldVeo 3.1Nano Banana ProMCP

The Brief
Bud Knows Ball already had a mascot: a photoreal CGI silverback with an established brand voice — deadpan, unbothered, permanently talking about sport. The job was to put him into short-form vertical video in the style of the AI Bigfoot vlogs, without a studio, a shoot, or an animator on retainer.
The Challenge
Character drift. Generate ten clips of the same character from text and you get ten slightly different characters — the face shifts, the fur changes, the brand stops being recognisable. That is the failure mode that kills AI mascot content, and it had to be solved before a single clip was worth publishing.
What We Built
- Two-stage generation pipeline — stage one produces a still keyframe from the existing brand art with an image model; stage two feeds that still in as the start frame for video. The face is locked before anything moves.
- A character bible with a fixed identity block — one canonical description of Bud, pasted verbatim into every prompt so the model is never asked to reinvent him.
- A format bible — shot grammar written down as rules rather than vibes: selfie POV at arm's length, wide-angle with mild barrel distortion, slightly low angle, constant handheld shake, available light only, 8 seconds, 9:16.
- A scripted season — twelve clips written to the format, each with a ready-to-run prompt, so producing the next one is a copy-paste rather than a creative restart.
- A production log — every generation recorded with its job ID and a verdict, so the prompts that work are known rather than remembered.
The Outcome
A repeatable process rather than a set of one-off experiments. Clips are produced from written scripts against a fixed character and a fixed shot grammar, which means the next batch does not depend on rediscovering what worked last time.
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