  3D pose transfer that aims to transfer the desired pose to a target mesh is
one of the most challenging 3D generation tasks. Previous attempts rely on
well-defined parametric human models or skeletal joints as driving pose
sources. However, to obtain those clean pose sources, cumbersome but necessary
pre-processing pipelines are inevitable, hindering implementations of the
real-time applications. This work is driven by the intuition that the
robustness of the model can be enhanced by introducing adversarial samples into
the training, leading to a more invulnerable model to the noisy inputs, which
even can be further extended to directly handling the real-world data like raw
point clouds/scans without intermediate processing. Furthermore, we propose a
novel 3D pose Masked Autoencoder (3D-PoseMAE), a customized MAE that
effectively learns 3D extrinsic presentations (i.e., pose). 3D-PoseMAE
facilitates learning from the aspect of extrinsic attributes by simultaneously
generating adversarial samples that perturb the model and learning the
arbitrary raw noisy poses via a multi-scale masking strategy. Both qualitative
and quantitative studies show that the transferred meshes given by our network
result in much better quality. Besides, we demonstrate the strong
generalizability of our method on various poses, different domains, and even
raw scans. Experimental results also show meaningful insights that the
intermediate adversarial samples generated in the training can successfully
attack the existing pose transfer models.
