  The main challenges of 3D pose transfer are: 1) Lack of paired training data
with different characters performing the same pose; 2) Disentangling pose and
shape information from the target mesh; 3) Difficulty in applying to meshes
with different topologies. We thus propose a novel weakly-supervised
keypoint-based framework to overcome these difficulties. Specifically, we use a
topology-agnostic keypoint detector with inverse kinematics to compute
transformations between the source and target meshes. Our method only requires
supervision on the keypoints, can be applied to meshes with different
topologies and is shape-invariant for the target which allows extraction of
pose-only information from the target meshes without transferring shape
information. We further design a cycle reconstruction to perform
self-supervised pose transfer without the need for ground truth deformed mesh
with the same pose and shape as the target and source, respectively. We
evaluate our approach on benchmark human and animal datasets, where we achieve
superior performance compared to the state-of-the-art unsupervised approaches
and even comparable performance with the fully supervised approaches. We test
on the more challenging Mixamo dataset to verify our approach's ability in
handling meshes with different topologies and complex clothes. Cross-dataset
evaluation further shows the strong generalization ability of our approach.
