Geometry-Contrastive Transformer for Generalized 3D Pose Transfer

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 MGcloth
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.