期刊
ACM TRANSACTIONS ON GRAPHICS
卷 39, 期 4, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3386569.3392471
关键词
Geometric Deep Learning; Surface Reconstruction; Shape Analysis
资金
- NSF-BSF grant [2017729]
- European research council [ERC-StG 757497]
- ISF [2366/16]
- Israel Science Foundation ISF-NSFC joint program [2217/15, 2472/17]
Recently, deep generative adversarial networks for image generation have advanced rapidly; yet, only a small amount of research has focused on generative models for irregular structures, particularly meshes. Nonetheless, mesh generation and synthesis remains a fundamental topic in computer graphics. In this work, we propose a novel framework for synthesizing geometric textures. It learns geometric texture statistics from local neighborhoods (Le., local triangular patches) of a single reference 3D model. It learns deep features on the faces of the input triangulation, which is used to subdivide and generate offsets across multiple scales, without parameterization of the reference or target mesh. Our network displaces mesh vertices in any direction (Le., in the normal and tangential direction), enabling synthesis of geometric textures, which cannot be expressed by a simple 2D displacement map. Learning and synthesizing on local geometric patches enables a genus-oblivious framework, facilitating texture transfer between shapes of different genus.
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