that learns hierarchical representations of a shape. An illustration of the architecture can be found in Fig \[fig:architecture\]. Leveraging the connectivity of the graph with spiral convolutional filters, we allow for local processing of each shape, while the hierarchical nature of the model allows learning in multiple scales. This way we manage to learn semantically meaningful representations and considerably reduce the number of parameters. Furthermore, we bypass the need to make assumptions about the distribution of the data.

Similar to traditional convolutional autoencoders, we make use of series of convolutional layers with small receptive fields followed by pooling and unpooling, for the encoder and the decoder respectively, where a decimated or upsampled version of the mesh is obtained each time and the features of the existing vertices are either aggregated or extrapolated. We follow [@coma] for the calculation of the features of the added vertices after upsampling, through interpolation by weighting the nearby vertices with barycentric coordinates. The network is trained by minimising the $L_1$ norm between the input and the predicted output.

Spiral Convolutional GAN
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In order to improve the synthesis of meshes of high resolution, thus increased detail, we extend our framework with a distribution matching scheme. 