Human shape correspondence with automatically predicted landmarks

We consider the problem of computing accurate point-to-point correspondences among a set of human bodies in similar posture using a landmark-free approach. The approach learns the locations of the anthropometric landmarks present in a database of human models in similar postures and uses this knowledge to automatically predict the locations of these anthropometric landmarks on a newly available scan. The predicted landmarks are then used to compute point-to-point correspondences between a template model and the newly available scan. This study conducts a large-scale evaluation to examine the accuracy of the computed correspondences. Furthermore, we show that the correspondences are accurate enough for the application of motion transfer.Peer reviewed: YesNRC publication: Ye