 In this manuscript, we present an approach that leverages a 3DMM to transfer its dense semantic annotation across a large set of heterogeneous 3D faces , establishing a dense correspondence between them. To this aim, 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 precisely fitting the 3DMM to arbitrary faces and transfer its semantic annotation  .
<coherence>  In this manuscript, we present an approach that leverages a 3DMM to transfer its dense semantic annotation across a large set of heterogeneous 3D faces , establishing a dense correspondence between them. To this aim, 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 precisely fitting the 3DMM to arbitrary faces and transfer its semantic annotation  .
 In this manuscript, we present an approach that leverages a 3DMM to transfer its dense semantic annotation across a large set of heterogeneous 3D faces . 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 precisely fitting the 3DMM to arbitrary faces and transfer its semantic annotation .