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DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects

MPS-Authors
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Tretschk,  Edgar
Computer Graphics, MPI for Informatics, Max Planck Society;

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Tewari,  Ayush
Computer Graphics, MPI for Informatics, Max Planck Society;

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Golyanik,  Vladislav
Computer Graphics, MPI for Informatics, Max Planck Society;

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Theobalt,  Christian
Computer Graphics, MPI for Informatics, Max Planck Society;

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Fulltext (public)

arXiv:1905.10290.pdf
(Preprint), 6MB

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There is no public supplementary material available
Citation

Tretschk, E., Tewari, A., Zollhöfer, M., Golyanik, V., & Theobalt, C. (2019). DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects. Retrieved from http://arxiv.org/abs/1905.10290.


Cite as: http://hdl.handle.net/21.11116/0000-0003-FE0C-3
Abstract
Mesh autoencoders are commonly used for dimensionality reduction, sampling and mesh modeling. We propose a general-purpose DEep MEsh Autoencoder (DEMEA) which adds a novel embedded deformation layer to a graph-convolutional mesh autoencoder. The embedded deformation layer (EDL) is a differentiable deformable geometric proxy which explicitly models point displacements of non-rigid deformations in a lower dimensional space and serves as a local rigidity regularizer. DEMEA decouples the parameterization of the deformation from the final mesh resolution since the deformation is defined over a lower dimensional embedded deformation graph. We perform a large-scale study on four different datasets of deformable objects. Reasoning about the local rigidity of meshes using EDL allows us to achieve higher-quality results for highly deformable objects, compared to directly regressing vertex positions. We demonstrate multiple applications of DEMEA, including non-rigid 3D reconstruction from depth and shading cues, non-rigid surface tracking, as well as the transfer of deformations over different meshes.