SDM-NET : Deep Generative Network for Structured Deformable Mesh

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Author(s)

  • Lin GAO
  • Jie YANG
  • Tong WU
  • Yu-Jie YUAN
  • Yu-Kun LAI
  • Hao ZHANG

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number243
Journal / PublicationACM Transactions on Graphics
Volume38
Issue number6
Publication statusPublished - Nov 2019

Link(s)

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.

Research Area(s)

  • Shape representation, variational autoencoder, structure, geometric details, generation

Citation Format(s)

SDM-NET : Deep Generative Network for Structured Deformable Mesh. / GAO, Lin; YANG, Jie; WU, Tong; YUAN, Yu-Jie; FU, Hongbo; LAI, Yu-Kun; ZHANG, Hao.

In: ACM Transactions on Graphics, Vol. 38, No. 6, 243, 11.2019.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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