Character creation through photography

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.