Before revision:  We propose a novel formulation to learn a set of sparse deformation components with local support on the face that, together with an original non-rigid deformation algorithm, allow the 3DMM to precisely fit unseen faces and transfer its semantic annotation to arbitrary 3D faces. We experimented our approach on three large and diverse datasets , showing it can effectively generalize to very different samples and accurately establish a dense correspondence even in presence of complex facial expressions.
After revision:  We propose a novel formulation to learn a set of sparse deformation components with local support on the face that, together with an original non-rigid deformation algorithm, allow the 3DMM to precisely fit unseen faces and transfer its semantic annotation . We extensively experimented our approach on three large and diverse datasets , showing it can effectively generalize to very different samples and accurately establish a dense correspondence even in presence of complex facial expressions.