  Pose transfer has been studied for decades, in which the pose of a source
mesh is applied to a target mesh. Particularly in this paper, we are interested
in transferring the pose of source human mesh to deform the target human mesh,
while the source and target meshes may have different identity information.
Traditional studies assume that the paired source and target meshes are existed
with the point-wise correspondences of user annotated landmarks/mesh points,
which requires heavy labelling efforts. On the other hand, the generalization
ability of deep models is limited, when the source and target meshes have
different identities. To break this limitation, we proposes the first neural
pose transfer model that solves the pose transfer via the latest technique for
image style transfer, leveraging the newly proposed component -- spatially
adaptive instance normalization. Our model does not require any correspondences
between the source and target meshes. Extensive experiments show that the
proposed model can effectively transfer deformation from source to target
meshes, and has good generalization ability to deal with unseen identities or
poses of meshes. Code is available at
https://github.com/jiashunwang/Neural-Pose-Transfer .
