NET: Deep Generative Networks for Textured Meshes
文献类型：期刊论文
| 作者 | Gao, Lin1,4; Wu, Tong1,4; Yu-Jie Yuan1,4; Ming-Xian Lin1,4; Yu-Kun Lai2; Zhang, Hao3 |
| 刊名 | ACM TRANSACTIONS ON GRAPHICS
 |
| 出版日期 | 2021-12-01 |
| 卷号 | 40期号:6页码:15 |
| 关键词 | Mesh representation Mesh texture Shape generation |
| ISSN号 | 0730-0301 |
| DOI | 10.1145/3478513.3480503 |
| 英文摘要 | We introduce TM-NET, a novel deep generative model for synthesizing textured meshes in a part-aware 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 separately 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 conditional autoregressive model 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. |
| 资助项目 | National Natural Science Foundation of China[62061136007] ; National Natural Science Foundation of China[61872440] ; Royal Society Newton Advanced Fellowship ; NAF[\R2\192151] ; Youth Innovation Promotion Association CAS |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000729846700068 |
| 出版者 | ASSOC COMPUTING MACHINERY |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/18355]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Gao, Lin |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing, Peoples R China 2.Cardiff Univ, Sch Comp Sci & Informat, Cardiff, Wales 3.Simon Fraser Univ, Sch Comp Sci, Burnaby, BC, Canada 4.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Gao, Lin,Wu, Tong,Yu-Jie Yuan,et al. NET: Deep Generative Networks for Textured Meshes[J]. ACM TRANSACTIONS ON GRAPHICS,2021,40(6):15. |
| APA | Gao, Lin,Wu, Tong,Yu-Jie Yuan,Ming-Xian Lin,Yu-Kun Lai,&Zhang, Hao.(2021).NET: Deep Generative Networks for Textured Meshes.ACM TRANSACTIONS ON GRAPHICS,40(6),15. |
| MLA | Gao, Lin,et al."NET: Deep Generative Networks for Textured Meshes".ACM TRANSACTIONS ON GRAPHICS 40.6(2021):15. |
入库方式： OAI收割
来源：计算技术研究所
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