Unsupervised Geometric Disentanglement for Surfaces via CFAN-VAE

Of recent interest in the deep learning community, generative models have proved to be powerful tools for many tasks including synthetic data generation and style transfer [1]. Geometric deep learning is a new field interested in extending such success of deep learning to non-Euclidean structured data [2]. The development of this field is timely given the recent proliferation of point cloud and mesh structured data obtained from sources such as laserscanners [3] and CAD software [4]. Particularly, mesh based convolutional autoencoders (MeshVAEs) are now a popular tool for generating surfaces [5, 6, 7, 8]. These models process a surface via geometric convolutions that respect its intrinsic geometry. With these VAEs achieving state-of-the-art performance on tasks such as reconstruction, more attention is being given towards tasks such as latent space interpretability. Geometric disentanglement, where the latent variables controlling intrinsic (properties independent of surface embedding) and extrinsic (properties dependent on surface embedding) geometry are separated [9], is an important open problem related to such interpretability. Applications include graphics, where a typical example is a disentangled latent space separating identity and pose in the case of human body generation [10, 11].