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Graph."],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:p>\n            We introduce TM-NET, a novel deep generative model for synthesizing\n            <jats:italic>textured meshes<\/jats:italic>\n            in a\n            <jats:italic>part-aware<\/jats:italic>\n            manner. Once trained, the network can generate novel textured meshes from scratch or predict textures for a given 3D mesh, without image guidance. Plausible and diverse textures can be generated for the same mesh part, while texture compatibility between parts in the same shape is achieved via conditional generation. Specifically, our method produces texture maps for individual shape parts, each as a deformable box, leading to a natural UV map with limited distortion. The network\n            <jats:italic>separately<\/jats:italic>\n            embeds part geometry (via a PartVAE) and part texture (via a TextureVAE) into their respective latent spaces, so as to facilitate learning texture probability distributions conditioned on geometry. We introduce a\n            <jats:italic>conditional autoregressive model<\/jats:italic>\n            for texture generation, which can be conditioned on both part geometry and textures already generated for other parts to achieve texture compatibility. To produce high-frequency texture details, our TextureVAE operates in a high-dimensional latent space via dictionary-based vector quantization. We also exploit transparencies in the texture as an effective means to model complex shape structures including topological details. Extensive experiments demonstrate the plausibility, quality, and diversity of the textures and geometries generated by our network, while avoiding inconsistency issues that are common to novel view synthesis methods.\n          <\/jats:p>","DOI":"10.1145\/3478513.3480503","type":"journal-article","created":{"date-parts":[[2021,12,10]],"date-time":"2021-12-10T18:29:20Z","timestamp":1639160960000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":36,"title":["TM-NET"],"prefix":"10.1145","volume":"40","author":[{"given":"Lin","family":"Gao","sequence":"first","affiliation":[{"name":"University of Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tong","family":"Wu","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Jie","family":"Yuan","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming-Xian","family":"Lin","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Kun","family":"Lai","sequence":"additional","affiliation":[{"name":"Cardiff University, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Simon Fraser University, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,12,10]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"International Conference on Machine Learning","volume":"80","author":"Achlioptas Panos","year":"2018","unstructured":"Panos Achlioptas , Olga Diamanti , Ioannis Mitliagkas , and Leonidas Guibas . 2018 . 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