Abstract
3D geometric contents are becoming increasingly popular. In this paper, we study the problem of analyzing deforming 3D meshes using deep neural networks. Deforming
3D meshes are flexible to represent 3D animation sequences
as well as collections of objects of the same category, allowing diverse shapes with large-scale non-linear deformations. We propose a novel framework which we call mesh
variational autoencoders (mesh VAE), to explore the probabilistic latent space of 3D surfaces. The framework is easy
to train, and requires very few training examples. We also
propose an extended model which allows flexibly adjusting
the significance of different latent variables by altering the
prior distribution. Extensive experiments demonstrate that
our general framework is able to learn a reasonable representation for a collection of deformable shapes, and produce competitive results for a variety of applications, including shape generation, shape interpolation, shape space
embedding and shape exploration, outperforming state-ofthe-art methods