论文标题
Skinningnet：用于剥皮预测的两流图卷积神经网络
SkinningNet: Two-Stream Graph Convolutional Neural Network for Skinning Prediction of Synthetic Characters
论文作者
论文摘要
这项工作介绍了Skinningnet，这是一种端到端的两流图神经网络结构，可从输入网格及其相关的骨骼中计算皮肤重量，而无需对所提供网格的形状类别和结构做出任何假设。虽然先前的方法预先计算网格和骨骼的手工制作的功能或假设骨架的固定拓扑，但提出的方法通过共同学习网格顶点和骨架关节之间的最佳关系，以端到端的可学习方式提取此信息。所提出的方法利用了新型多聚聚物图卷积的好处，该卷积结合了消息通话方案的汇总步骤中不同聚合器的结果，从而帮助操作推广到不看到的拓扑。实验结果证明了我们新型建筑的贡献的有效性，Skinningnet的表现优于当前最新替代方案。
This work presents SkinningNet, an end-to-end Two-Stream Graph Neural Network architecture that computes skinning weights from an input mesh and its associated skeleton, without making any assumptions on shape class and structure of the provided mesh. Whereas previous methods pre-compute handcrafted features that relate the mesh and the skeleton or assume a fixed topology of the skeleton, the proposed method extracts this information in an end-to-end learnable fashion by jointly learning the best relationship between mesh vertices and skeleton joints. The proposed method exploits the benefits of the novel Multi-Aggregator Graph Convolution that combines the results of different aggregators during the summarizing step of the Message-Passing scheme, helping the operation to generalize for unseen topologies. Experimental results demonstrate the effectiveness of the contributions of our novel architecture, with SkinningNet outperforming current state-of-the-art alternatives.
