 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 or unseen deformations. As main outcome of this work, we build a heterogeneous, large-scale 3DMM from more than 9,000 fully registered scans obtained  joining the three datasets together .
<coherence>  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 or unseen deformations. As main outcome of this work, we build a heterogeneous, large-scale 3DMM from more than 9,000 fully registered scans obtained  joining the three datasets together .
 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 . The accuracy of the dense registration is demonstrated by building a heterogeneous, large-scale 3DMM from more than 9,000 fully registered scans obtained joining the three datasets together .