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SDM-NET: Deep generative network for structured deformable mesh

Gao, Lin, Yang, Jie, Wu, Tong, Yuan, Yu-Jie, Fu, Hongbo, Lai, Yukun and Zhang, Hao 2019. SDM-NET: Deep generative network for structured deformable mesh. ACM Transactions on Graphics 38 , 243. 10.1145/3355089.3356488

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Abstract

We introduce SDM-NET, a deep generative neural network which produces structured deformable meshes. Specifically, the network is trained to generate a spatial arrangement of closed, deformable mesh parts, which respects the global part structure of a shape collection, e.g., chairs, airplanes, etc. Our key observation is that while the overall structure of a 3D shape can be complex, the shape can usually be decomposed into a set of parts, each homeomorphic to a box, and the finer-scale geometry of the part can be recovered by deforming the box. The architecture of SDM-NET is that of a two-level variational autoencoder (VAE). At the part level, a PartVAE learns a deformable model of part geometries. At the structural level, we train a Structured Parts VAE (SP-VAE), which jointly learns the part structure of a shape collection and the part geometries, ensuring the coherence between global shape structure and surface details. Through extensive experiments and comparisons with the state-of-the-art deep generative models of shapes, we demonstrate the superiority of SDM-NET in generating meshes with visual quality, flexible topology, and meaningful structures, benefiting shape interpolation and other subsequent modeling tasks.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Additional Information: "© ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution.
Publisher: Association for Computing Machinery (ACM)
ISSN: 0730-0301
Date of First Compliant Deposit: 16 September 2019
Date of Acceptance: 28 August 2019
Last Modified: 27 Feb 2020 15:04
URI: http://orca-mwe.cf.ac.uk/id/eprint/125459

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