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Mesh-based variational autoencoders for localized deformation component analysis

Tan, Qingyang, Zhang, Ling-Xiao, Yang, Jie, Lai, Yu-Kun ORCID: and Gao, Lin 2022. Mesh-based variational autoencoders for localized deformation component analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (10) , pp. 6297-6310. 10.1109/TPAMI.2021.3085887

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Spatially localized deformation components are very useful for shape analysis and synthesis in 3D geometry processing. Several methods have recently been developed, with an aim to extract intuitive and interpretable deformation components. However, these techniques suffer from fundamental limitations especially for meshes with noise or large-scale nonlinear deformations, and may not always be able to identify important deformation components. In this paper we propose a novel mesh-based variational autoencoder architecture that is able to cope with meshes with irregular connectivity and nonlinear deformations. To help localize deformations, we introduce sparse regularization along with spectral graph convolutional operations. Through modifying the regularization formulation and allowing dynamic change of sparsity ranges, we improve the visual quality and reconstruction ability. Our system also provides a nonlinear approach to reconstruction of meshes using the extracted basis, which is more effective than the current linear combination approach. We further develop a neural shape editing method, achieving shape editing and deformation component extraction in a unified framework and ensuring plausibility of the edited shapes. Extensive experiments show that our method outperforms state-of-the-art methods in both qualitative and quantitative evaluations. We also demonstrate the effectiveness of our method for neural shape editing.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 0162-8828
Funders: The Royal Society
Date of First Compliant Deposit: 19 June 2021
Date of Acceptance: 16 May 2021
Last Modified: 04 May 2023 10:25

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