Strokes2Deform: Physics-informed learning of deformation fields on 3D stroke clouds

• A learning-based model that enables deformation-aware 3D sketching without requiring reconstruction-and-simulation. • A synthetic dataset of 40K sketch-deformation pairs spanning 3D sketches of architectural thin-shell structures and their corresponding deformation fields derived from Finite Element Analysis (FEA). • A dual-head physics-informed neural network that decouples deformation field estimation into predicting unit-length displacement vectors and scalar displacement magnitudes. • Physics-guided regularization terms incorporating stretching and bending as the two modes of deformation.