Deformation component analysis is a fundamental problem in geometry processing and shape understanding. Existing approaches mainly extract deformation components in local regions at a similar scale while deformations of real-world objects are usually distributed in a multi-scale manner. In this paper, 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. Quantitative and qualitative evaluations show that our method outperforms state-of-the-art methods. Furthermore, with the multiscale deformation components extracted by our method, the user can edit shapes in a coarse-to-fine fashion which facilitates effective modeling of new shapes.
@article{arxiv.2012.02459,
title = {Multiscale Mesh Deformation Component Analysis with Attention-based Autoencoders},
author = {Jie Yang and Lin Gao and Qingyang Tan and Yihua Huang and Shihong Xia and Yu-Kun Lai},
journal= {arXiv preprint arXiv:2012.02459},
year = {2020}
}