  With the strength of deep generative models, 3D pose transfer regains
intensive research interests in recent years. Existing methods mainly rely on a
variety of constraints to achieve the pose transfer over 3D meshes, e.g., the
need for manually encoding for shape and pose disentanglement. In this paper,
we present an unsupervised approach to conduct the pose transfer between any
arbitrate given 3D meshes. Specifically, a novel Intrinsic-Extrinsic Preserved
Generative Adversarial Network (IEP-GAN) is presented for both intrinsic (i.e.,
shape) and extrinsic (i.e., pose) information preservation. Extrinsically, we
propose a co-occurrence discriminator to capture the structural/pose invariance
from distinct Laplacians of the mesh. Meanwhile, intrinsically, a local
intrinsic-preserved loss is introduced to preserve the geodesic priors while
avoiding heavy computations. At last, we show the possibility of using IEP-GAN
to manipulate 3D human meshes in various ways, including pose transfer,
identity swapping and pose interpolation with latent code vector arithmetic.
The extensive experiments on various 3D datasets of humans, animals and hands
qualitatively and quantitatively demonstrate the generality of our approach.
Our proposed model produces better results and is substantially more efficient
compared to recent state-of-the-art methods. Code is available:
https://github.com/mikecheninoulu/Unsupervised_IEPGAN
