RAS: A Data‐Driven Rigidity‐Aware Skinning Model For 3D Facial Animation

Abstract

We present a novel data‐driven skinning model—rigidity‐aware skinning (RAS) model, for simulating both active and passive 3D facial animation of different identities in real time. Our model builds upon a linear blend skinning (LBS) scheme, where the bone set and skinning weights are shared for diverse identities and learned from the data via a sparse and localized skinning decomposition algorithm. Our model characterizes the animated face into the active expression and the passive deformation: The former is represented by an LBS‐based multi‐linear model learned from the FaceWareHouse data set, and the latter is represented by a spatially varying as‐rigid‐as‐possible deformation applied to the LBS‐based multi‐linear model, whose rigidity parameters are learned from the data by a novel rigidity estimation algorithm. Our RAS model is not only generic and expressive for faithfully modelling medium‐scale facial deformation, but also compact and lightweight for generating vivid facial animation in real time. We validate the efficiency and effectiveness of our RAS model for real‐time 3D facial animation and expression editing.