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Variational autoencoders for deforming 3D mesh models

Tan, Qingyang, Gao, Lin, Lai, Yukun ORCID: and Xia, Shihong 2018. Variational autoencoders for deforming 3D mesh models. Presented at: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Lake Salt City, USA, 18-22 Jun 2018.

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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.

Item Type: Conference or Workshop Item (Paper)
Status: In Press
Schools: Computer Science & Informatics
Funders: Royal Society
Date of First Compliant Deposit: 29 March 2018
Date of Acceptance: 19 February 2018
Last Modified: 23 Oct 2022 13:20

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