VARIATIONAL AUTO-ENCODERS WITHOUT GRAPH COARSENING FOR FINE MESH LEARNING

In this paper, we propose a Variational Auto-Encoder able to correctly reconstruct a fine mesh from a very low-dimensional latent space. The architecture avoids the usual coarsening of the graph and relies on pooling layers for the decoding phase and on the mean values of the training set for the up-sampling phase. We select new operators compared to previous work, and in particular, we define a new Dirac operator which can be extended to different types of graph structured data. We show the improvements over the previous operators and compare the results with the current benchmark on the Coma Dataset.