Learning of garment deformations in a collision-free space

Abstract

Systems and methods are provided that learn garment deformations such that they are essentially collision free. A diffused, volumetric body model representation of the underlying body together with the construction of a subspace for the garment model that yields a differentiable, canonical space configuration. This subspace is used for the regression of the garment model deformation and its dynamics. In this way, garment model deformations are predicted avoiding collisions, and the complexity for inference is reduced, such that a learned representation yields higher quality than previously achievable. The generated garments exhibit a large amount of spatial and temporal detail, and can be produced extremely quickly via the pre-trained networks.