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Graph."],"published-print":{"date-parts":[[2019,12,31]]},"abstract":"<jats:p>\n                    We introduce SDM-NET, a deep generative neural network which produces\n                    <jats:italic>structured deformable meshes.<\/jats:italic>\n                    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\n                    <jats:italic>deforming<\/jats:italic>\n                    the box. The architecture of SDM-NET is that of a\n                    <jats:italic>two-level variational autoencoder<\/jats:italic>\n                    (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\n                    <jats:italic>jointly<\/jats:italic>\n                    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.\n                  <\/jats:p>","DOI":"10.1145\/3355089.3356488","type":"journal-article","created":{"date-parts":[[2019,11,8]],"date-time":"2019-11-08T15:27:58Z","timestamp":1573226878000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":189,"title":["SDM-NET"],"prefix":"10.1145","volume":"38","author":[{"given":"Lin","family":"Gao","sequence":"first","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences (CAS)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, CAS and University of Chinese Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tong","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, CAS and University of Chinese Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Jie","family":"Yuan","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, CAS and University of Chinese Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongbo","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Creative Media, City University of Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Kun","family":"Lai","sequence":"additional","affiliation":[{"name":"School of Computer Science and Informatics, Cardiff University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computing Science, Simon Fraser University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,11,8]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"International Conference on Machine Learning (ICML)","volume":"80","author":"Achlioptas Panos","year":"2018","unstructured":"Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas. 2018. 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