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The goal of 3D pose transfer is to transfer the pose from the source mesh to
the target mesh while preserving the identity information (e.g., face, body
shape) of the target mesh. Deep learning-based methods improved the efficiency
and performance of 3D pose transfer. However, most of them are trained under
the supervision of the ground truth, whose availability is limited in
real-world scenarios. In this work, we present X-DualNet, a simple yet
effective approach that enables unsupervised 3D pose transfer. In X-DualNet, we
introduce a generator $G$ which contains correspondence learning and pose
transfer modules to achieve 3D pose transfer. We learn the shape correspondence
by solving an optimal transport problem without any key point annotations and
generate high-quality meshes with our elastic instance normalization (ElaIN) in
the pose transfer module. With $G$ as the basic component, we propose a cross
consistency learning scheme and a dual reconstruction objective to learn the
pose transfer without supervision. Besides that, we also adopt an
as-rigid-as-possible deformer in the training process to fine-tune the body
shape of the generated results. Extensive experiments on human and animal data
demonstrate that our framework can successfully achieve comparable performance
as the state-of-the-art supervised approaches.