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Published October 27, 2021 | Version v1
Conference paper Open

Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer

  • 1. University of Oulu
  • 2. ETH
  • 3. University of Trento, Italy

Description

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 cooccurrence
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-ofthe-
art methods. Code is available: https://github.
com/mikecheninoulu/Unsupervised_IEPGAN

Files

Chen_Intrinsic-Extrinsic_Preserved_GANs_for_Unsupervised_3D_Pose_Transfer_ICCV_2021_paper (3).pdf