Multiscale Mesh Deformation Component Analysis with Attention-based Autoencoders

• URL: http://arxiv.org/abs/2012.02459v1
• Date: Fri, 4 Dec 2020 08:30:57 GMT
• Title: Multiscale Mesh Deformation Component Analysis with Attention-based Autoencoders
• Authors: Jie Yang, Lin Gao, Qingyang Tan, Yihua Huang, Shihong Xia and Yu-Kun Lai
• Abstract summary: 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. With our method, the user can edit shapes in a coarse-to-fine fashion which facilitates effective modeling of new shapes.
• Score: 49.62443496989065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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