  Shape correspondence from 3D deformation learning has attracted appealing
academy interests recently. Nevertheless, current deep learning based methods
require the supervision of dense annotations to learn per-point translations,
which severely overparameterize the deformation process. Moreover, they fail to
capture local geometric details of original shape via global feature embedding.
To address these challenges, we develop a new Unsupervised Dense Deformation
Embedding Network (i.e., UD^2E-Net), which learns to predict deformations
between non-rigid shapes from dense local features. Since it is non-trivial to
match deformation-variant local features for deformation prediction, we develop
an Extrinsic-Intrinsic Autoencoder to frst encode extrinsic geometric features
from source into intrinsic coordinates in a shared canonical shape, with which
the decoder then synthesizes corresponding target features. Moreover, a bounded
maximum mean discrepancy loss is developed to mitigate the distribution
divergence between the synthesized and original features. To learn natural
deformation without dense supervision, we introduce a coarse parameterized
deformation graph, for which a novel trace and propagation algorithm is
proposed to improve both the quality and effciency of the deformation. Our
UD^2E-Net outperforms state-of-the-art unsupervised methods by 24% on Faust
Inter challenge and even supervised methods by 13% on Faust Intra challenge.
