A 3D Body from Eight Questions — No Photo, No GPU

Clad built a small MLP that predicts 58 body shape parameters from just 8 questionnaire inputs, achieving 0.3 cm height MAE and 0.3–0.5 kg mass MAE — outperforming both a photo-based pipeline and height+weight regression on circumferences. The key innovation is a differentiable physics loss: the MLP's outputs are passed through the Anny body model's forward pass (blendshapes → vertices → volume → mass), so mass errors backpropagate through all volume-related parameters jointly. The model is tiny (~85 KB), trains in ~60 minutes on a laptop, and runs in milliseconds on CPU. Major lessons include fixing body density calculations per gender using the Siri two-component model, discovering that a training/inference distribution mismatch on ancestry blendshapes caused a 3 kg noise floor, and finding that dataset quality and evaluation rigor mattered far more than model architecture.