  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.
