# DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects

## TL;DR

DEMEA introduces a novel deep mesh autoencoder with an embedded deformation layer that explicitly models non-rigid deformations, improving mesh modeling and reconstruction for deformable objects.

## Contribution

The paper presents DEMEA, a new mesh autoencoder with an embedded deformation layer that decouples deformation parameterization from mesh resolution, enhancing modeling of non-rigid deformations.

## Key findings

- Higher quality results for highly deformable objects.
- Effective non-rigid 3D reconstruction from depth and shading.
- Successful deformation transfer across different meshes.

## Abstract

Mesh autoencoders are commonly used for dimensionality reduction, sampling and mesh modeling. We propose a general-purpose DEep MEsh Autoencoder (DEMEA) which adds a novel embedded deformation layer to a graph-convolutional mesh autoencoder. The embedded deformation layer (EDL) is a differentiable deformable geometric proxy which explicitly models point displacements of non-rigid deformations in a lower dimensional space and serves as a local rigidity regularizer. DEMEA decouples the parameterization of the deformation from the final mesh resolution since the deformation is defined over a lower dimensional embedded deformation graph. We perform a large-scale study on four different datasets of deformable objects. Reasoning about the local rigidity of meshes using EDL allows us to achieve higher-quality results for highly deformable objects, compared to directly regressing vertex positions. We demonstrate multiple applications of DEMEA, including non-rigid 3D reconstruction from depth and shading cues, non-rigid surface tracking, as well as the transfer of deformations over different meshes.