Multiscale Mesh Deformation Component Analysis with Attention-based Autoencoders

We propose a novel method to exact multiscale deformation components automatically with a stacked attention-based autoencoder. The attention mechanism is designed to learn to softly weight multi-scale deformation components in active deformation regions, and the stacked attention-based autoencoder is learned to represent the deformation components at different scales.

Mesh-based Autoencoders for Localized Deformation Component Analysis

We propose a novel mesh-based autoencoder architecture that is able to cope with meshes with irregular topology. We introduce sparse regularization in this framework, which along with convolutional operations, helps localize mesh deformations. Our framework is capable of extracting localized deformation components from mesh data sets with large-scale deformations and is robust to noise.