In particular, we propose a mesh Wasserstein GAN [@pmlr-v70-arjovsky17a] with gradient penalty to enforce the Lipschitz constraint [@gulrajani2017improved], that is trained to minimise the wasserstein divergence between the real distribution of the meshes and the distribution of those produced by the generator network. The generator and discriminator architectures, have the same structure as the decoder and the encoder of the Neural3DMM respectively. Via this framework, we obtain two additional properties that are inherently absent from the autoencoder: high frequency detail and a straightforward way to sample from the latent space.

Evaluation
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In this section, we showcase the effectiveness of our proposed method on a variety of shape datasets. We conduct a series of ablation studies in order to compare our operator to other Graph Neural Networks, by using the same autoencoder architecture. Fist, we demonstrate the inherent higher capacity of spiral convolutions compared to ChebNet (spectral). Moreover, we discuss the advantages of our method compared to soft-attention based Graph Neural Networks, such as patch-operator based. Finally, we show the importance of the consistency of the ordering by comparing our method to different variants of the method proposed in [@spirals].

Furthermore, we quantitatively show that our method can yield better 