17/08/2020

Deep geometric texture synthesis

Amir Hertz, Rana Hanocka, Raja Giryes, Daniel Cohen-Or

Keywords: surface reconstruction, shape analysis, geometric deep learning

Abstract Paper Similar Papers

Abstract: 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 (i.e., 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 (i.e., 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.

The video of this talk cannot be embedded. You can watch it here:
https://dl.acm.org/doi/10.1145/3386569.3392471#sec-supp
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at SIGGRAPH 2020 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 13:25 
 0
 0
 0
 0
 16:06 
 0
 0
 0
 0
 10:41 
 0
 0
 0
 0
 13:07 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 4:56 
 0
 0
 0
 0
 14:05 
 0
 0
 0
 0
 3:22 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 4:56 
 0
 0
 0
 0
 4:59 
 0
 0
 0
 0
 4:56 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 2:43 
 0
 0
 0
 0
 3:24 
 0
 0
 0
 0
 19:51 
 0
 0
 0
 0
 3:16 
 0
 0
 0
 0
 12:20 
 0
 0
 0
 0
 3:25 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 0:58 
 0
 0
 0
 0
 4:57 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 4:39 
 0
 0
 0
 0
 9:45 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 16:47 
 0
 0
 0
 0
 4:35 
 0
 0
 0
 0
 3:22 
 0
 0
 0
 0
 2:59 
 0
 0
 0
 0
 16:49 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 5:08 
 0
 0
 0
 0
 4:34 
 0
 0
 0
 0
 3:17 
 0
 0
 0
 0
 13:04