TM-NET
نویسندگان
چکیده
We introduce TM-NET, a novel deep generative model for synthesizing textured meshes in part-aware manner. Once trained, the network can generate from scratch or predict textures given 3D mesh, without image guidance. Plausible and diverse be generated same mesh part, while texture compatibility between parts shape is achieved via conditional generation. Specifically, our method produces maps individual parts, each as deformable box, leading to natural UV map with limited distortion. The separately embeds part geometry (via PartVAE) TextureVAE) into their respective latent spaces, so facilitate learning probability distributions conditioned on geometry. autoregressive generation, which both already other achieve compatibility. To produce high-frequency details, TextureVAE operates high-dimensional space dictionary-based vector quantization. also exploit transparencies an effective means complex structures including topological details. Extensive experiments demonstrate plausibility, quality, diversity of geometries by network, avoiding inconsistency issues that are common view synthesis methods.
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ژورنال
عنوان ژورنال: ACM Transactions on Graphics
سال: 2021
ISSN: ['0730-0301', '1557-7368']
DOI: https://doi.org/10.1145/3478513.3480503