Our virtual haircut engine uses multi-stage computer vision pipelines: face geometry extraction, hair region segmentation, and depth approximation.
We combine landmark detection with volumetric estimation to respect natural growth patterns when overlaying new styles.
Generative refinement models adjust lighting, strand continuity, and contour blending for realism.
Privacy: photos are processed ephemerally—no long-term storage for style trials unless explicitly saved.
Model training: anonymized hair shape datasets + synthetic augmentation drive adaptability across demographics.
Confidence scoring helps us show style recommendations likely to work with your density, curl type, and hairline.
Future roadmap includes personalized maintenance projections and growth simulation for transition styles.
Responsible AI: we provide transparency on limitations—very complex braids and extreme fantasy colors can still render imperfect artifacts.
Your feedback loop improves prediction accuracy; rating styles tunes similarity search embeddings.
The system goal: empower informed decisions before committing to a physical change.