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

DSFFNet: Dual-Side Feature Fusion Network for 3D Pose Transfer