Sparse constraints over Animatable subspaces

Abstract

In this chapter, we present a cage-based inversion process for modeling tasks. This novel core methodology estimates coherent enclosed model deformation via sparse positional constraints. In particular, we investigate a new technique mixing surface-and-space deformation in order to fit input feature correspondences. Moreover, user-specified constraints are transferred into the cage domain with a well-adapted and detached Laplacian-type regularization. Thus, we suggest a novel algorithm to easily edit shape deformation using the inverse kinematics paradigm applied in geometric space. Finally, an intuitive user interface is proposed to allow users to easily specify desired screen-space constraints.