For non-Euclidean data such as meshes of humans, a prominent task for generative models is geometric disentanglement; the separation of latent codes for intrinsic (i.e. identity) and extrinsic(i.e. pose) geometry . This work introduces a novel 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 and parallel transport convolution. CFAN-VAE achieves this geometric disentanglement in an unsupervised way, as it does not require label information on the identity or pose during training  . Our comprehensive experiments, including reconstruction, interpolation, generation, and canonical correlation analysis, validate the effectiveness of the unsupervised geometric disentanglement. We also successfully detect and recover geometric disentanglement in mesh convolutional autoencoders that encode xyz-coordinates directly by registering its latent space to that of CFAN-VAE.
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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For non-Euclidean data such as meshes of humans, a prominent task for generative models is geometric disentanglement;
Geometric disentanglement,
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, 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.
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novel
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and parallel transport convolution. CFAN-VAE achieves this geometric disentanglement in an unsupervised way, as it does not require
. Requiring no
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, CFAN-VAE achieves geometric disentanglement in an unsupervisedway
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canonical correlation analysis, validate the effectiveness of the
identity/pose transfer, demonstrate CFAN-VAE achieves state-of-the-art performance on
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and recover
a level of