3D pose transfer solves the problem of additional input and correspondence of traditional deformation transfer, only the source and target meshes need to be input, and the pose of the source mesh can be transferred to the target mesh. Some lightweight methods proposed in recent years consume less memory but cause spikes and distortions for some unseen poses, while others are costly in training due to the inclusion of large matrix multiplication and adversarial networks. In addition, the meshes with different numbers of vertices also increase the difficulty of pose transfer. In this work, we propose a Dual-Side Feature Fusion Pose Transfer Network to improve the pose transfer accuracy of the lightweight method. Our method takes the pose features as one of the side inputs to the decoding network and fuses them into the target mesh layer by layer at multiple scales. Our proposed Feature Fusion Adaptive Instance Normalization has the characteristic of having two side input channels that fuse pose features and identity features as denormalization parameters, thus enhancing the pose transfer capability of the network. Extensive experimental results show that our proposed method has stronger pose transfer capability than state-of-the-art methods while maintaining a lightweight network structure, and can converge faster.

翻译：三维姿态迁移解决了传统形变迁移中需要额外输入和对应关系的问题，仅需输入源网格和目标网格，即可将源网格的姿态迁移至目标网格。近年来提出的轻量级方法虽内存消耗较低，但对未见过姿态易产生尖刺与形变扭曲；而另一些方法因包含大型矩阵乘法及对抗网络导致训练成本过高。此外，顶点数量不同的网格也增加了姿态迁移的难度。本文提出一种双端特征融合姿态迁移网络，旨在提升轻量级方法的姿态迁移精度。本方法将姿态特征作为解码网络的侧端输入之一，并在多尺度上逐层将其融合至目标网格中。我们提出的特征融合自适应实例归一化具有双端输入通道特性，可将姿态特征与身份特征融合为反归一化参数，从而增强网络的姿态迁移能力。大量实验结果表明，本方法在保持轻量级网络结构的同时，相比现有最先进方法具有更强的姿态迁移能力，且收敛速度更快。