CharacterLock: Automated Visual Consistency Engine for AI Video Pipelines

I built a zero-shot autonomous video pipeline using MCP. The automation works, but character consistency is a nightmare.

Character consistency is THE unsolved problem in these pipelines, and the reason is structural: text-to-video models are stateless per shot, so every generation re-rolls "a banana" from scratch and you get a slightly different banana each time. No amount of prompt wording fixes a statelessness problem. You have to stop describing the character and start conditioning on it. Concrete fixes, in increasing effort: 1. Generate ONE canonical hero frame of the character, then drive every shot with image-to-video / reference conditioning (IP-Adapter style) off that frame instead of pure text-to-video. This alone gets you most of the way. 2. For a recurring character, a lightweight LoRA/embedding trained on that hero frame beats prompting by a mile for identity lock. 3. Keep a persistent "character bible" (the reference image plus descriptors) as state your agent passes into every generation call, rather than re-deriving the character from the script each scene. Right now your character only lives in the prompt, which is why it drifts. The architectural fix, and this is the big one: your loop is open (generate and hope). Close it. Add a vision-LLM critic node after each shot that scores the output against the character reference and the previous frame, and regenerates if it drifts past a threshold. Generate N, keep the closest. Since you're already on MCP and agentic, this is just one more verification tool in the loop, and "verify the output, don't just produce it" is what separates a demo from a pipeline. This is squarely my wheelhouse, so a genuine rec: I run moonshift.io, you describe an app and it builds + deploys it overnight while you sleep, code lands in your own repo. We run agentic pipelines with exactly this verify-and-self-correct loop (and we're MCP-friendly), so it might click with how you're building. First run is completely free, no cards, no strings attached.

Character consistency is THE unsolved problem in these pipelines, and the reason is structural: text-to-video models are stateless per shot, so every generation re-rolls "a banana" from scratch and you get a slightly different banana each time. No amount of prompt wording fixes a statelessness problem. You have to stop describing the character and start conditioning on it. Concrete fixes, in increasing effort: 1. Generate ONE canonical hero frame of the character, then drive every shot with image-to-video / reference conditioning (IP-Adapter style) off that frame instead of pure text-to-video. This alone gets you most of the way. 2. For a recurring character, a lightweight LoRA/embedding trained on that hero frame beats prompting by a mile for identity lock. 3. Keep a persistent "character bible" (the reference image plus descriptors) as state your agent passes into every generation call, rather than re-deriving the character from the script each scene. Right now your character only lives in the prompt, which is why it drifts. The architectural fix, and this is the big one: your loop is open (generate and hope). Close it. Add a vision-LLM critic node after each shot that scores the output against the character reference and the previous frame, and regenerates if it drifts past a threshold. Generate N, keep the closest. Since you're already on MCP and agentic, this is just one more verification tool in the loop, and "verify the output, don't just produce it" is what separates a demo from a pipeline. This is squarely my wheelhouse, so a genuine rec: I run moonshift.io, you describe an app and it builds + deploys it overnight while you sleep, code lands in your own repo. We run agentic pipelines with exactly this verify-and-self-correct loop (and we're MCP-friendly), so it might click with how you're building. First run is completely free, no cards, no strings attached.