  To solve the problem of pose distortion in the forward propagation of pose
features in existing methods, this pa-per proposes a Dual-Side Feature Fusion
Network for pose transfer (DSFFNet). Firstly, a fixed-length pose code is
extracted from the source mesh by a pose encoder and combined with the target
vertices to form a mixed feature; Then, a Feature Fusion Adaptive Instance
Normalization module (FFAdaIN) is designed, which can process both pose and
identity features simultaneously, so that the pose features can be compensated
in layer-by-layer for-ward propagation, thus solving the pose distortion
problem; Finally, using the mesh decoder composed of this module, the pose are
gradually transferred to the target mesh. Experimental results on SMPL, SMAL,
FAUST and MultiGarment datasets show that DSFFNet successfully solves the pose
distortion problem while maintaining a smaller network structure with stronger
pose transfer capability and faster convergence speed, and can adapt to meshes
with different numbers of vertices. Code is available at
https://github.com/YikiDragon/DSFFNet
