Unsupervised Geometric Disentanglement for Surfaces via CFAN-VAE

• URL: http://arxiv.org/abs/2005.11622v2
• Date: Thu, 10 Dec 2020 01:50:38 GMT
• Title: Unsupervised Geometric Disentanglement for Surfaces via CFAN-VAE
• Authors: N. Joseph Tatro, Stefan C. Schonsheck, Rongjie Lai
• Abstract summary: We propose CFAN-VAE, a novel architecture that disentangles identity and pose using the CFAN feature. Requiring no label information on the identity or pose during training, CFAN-VAE achieves geometric disentanglement in an unsupervisedway. We also successfully detect a level of geometric disentanglement in mesh convolutional autoencoders that encode xyz-coordinates directly by registering its latent space to that of CFAN-VAE.
• Score: 7.5714374873825765
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Geometric disentanglement, the separation of latent codes for intrinsic (i.e. identity) and extrinsic(i.e. pose) geometry, is a prominent task for generative models of non-Euclidean data such as 3D deformable models. It provides greater interpretability of the latent space, and leads to more control in generation. This work introduces a mesh feature, the conformal factor and normal feature (CFAN),for use in mesh convolutional autoencoders. We further propose CFAN-VAE, a novel architecture that disentangles identity and pose using the CFAN feature. Requiring no label information on the identity or pose during training, CFAN-VAE achieves geometric disentanglement in an unsupervisedway. Our comprehensive experiments, including reconstruction, interpolation, generation, and identity/pose transfer, demonstrate CFAN-VAE achieves state-of-the-art performance on unsupervised geometric disentanglement. We also successfully detect a level of geometric disentanglement in mesh convolutional autoencoders that encode xyz-coordinates directly by registering its latent space to that of CFAN-VAE.

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