Case study
Character creation through photography
How a filmmaker-first photoshoot, framed lighting, and detailed captions helped stabilize identity before any prompts were written.
This case study is still being iterated on as we refine the production notes.
Casting-level photography before prompting
Instead of inventing a character through prompts, we treated the shoot just like casting and wardrobe tests: controlled angles, consistent lighting, and a clear reference library.
Step 1: Photograph the character
- Front portrait
- Three-quarter portrait
- Side profile
- Full body standing
- Full body walking motion
- Emotion variations
Consistent lighting prevents the training data from introducing noise, just like keeping wardrobe tests under the same key light.
Step 2: Capture lens and distance variations
Close/medium/wide framings teach the LoRA how the character should read across shot sizes, mirroring how cinematographers test multiple lenses.
Step 3: Caption with extreme detail
Captions describe face structure, hairstyle, body proportions, clothing textures, color tones, and lighting so the model learns identity rather than guessing.
Step 4: Train the identity model
Once the dataset is stable, the identity model is trained and observed over multiple passes to detect drift. The early results surfaced the same pain point: open-source training tools are powerful but require filmmaker-friendly abstractions.