  We present a customized 3D mesh Transformer model for the pose transfer task.
As the 3D pose transfer essentially is a deformation procedure dependent on the
given meshes, the intuition of this work is to perceive the geometric
inconsistency between the given meshes with the powerful self-attention
mechanism. Specifically, we propose a novel geometry-contrastive Transformer
that has an efficient 3D structured perceiving ability to the global geometric
inconsistencies across the given meshes. Moreover, locally, a simple yet
efficient central geodesic contrastive loss is further proposed to improve the
regional geometric-inconsistency learning. At last, we present a latent
isometric regularization module together with a novel semi-synthesized dataset
for the cross-dataset 3D pose transfer task towards unknown spaces. The massive
experimental results prove the efficacy of our approach by showing
state-of-the-art quantitative performances on SMPL-NPT, FAUST and our new
proposed dataset SMG-3D datasets, as well as promising qualitative results on
MG-cloth and SMAL datasets. It's demonstrated that our method can achieve
robust 3D pose transfer and be generalized to challenging meshes from unknown
spaces on cross-dataset tasks. The code and dataset are made available. Code is
available: https://github.com/mikecheninoulu/CGT.
