Variational Autoencoders for Localized Mesh Deformation Component Analysis

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 limitation...

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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence Jg. 44; H. 10; S. 6297 - 6310
Hauptverfasser: Tan, Qingyang, Zhang, Ling-Xiao, Yang, Jie, Lai, Yu-Kun, Gao, Lin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.10.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
3D meshes
Convolution
Deformation analysis
Editing
Geometry
graph convolution
Principal component analysis
Reconstruction
Regularization
Shape
Solid modeling
sparsity regularization
Strain
Three-dimensional displays
variational autoencoder
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
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Zusammenfassung: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 mesh-based variational autoencoder architecture that is able to cope with meshes with irregular connectivity and nonlinear deformations, assuming that the analyzed dataset contains meshes with the same vertex connectivity, which is common for deformation analysis. To help localize deformations, we introduce sparse regularization in this framework, 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 of the extracted deformation components. 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. As an important application of localized deformation components and a novel approach on its own, 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.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2021.3085887