The visual richness of computer graphics applications is frequently limited by the difficulty of obtaining high-quality, detailed 3D models. This paper proposes a method for realistically transferring details (specifically, displacement maps) from existing high-quality 3D models to simple shapes that may be created with easy-to-learn modeling tools. Our key insight is to use metric learning to find a combination of geometric features that successfully predicts detail-map similarities on the source mesh; we use the learned feature combination to drive the detail transfer. The latter uses a variant of multi-resolution non-parametric texture synthesis, augmented by a high-frequency detail transfer step in texture space. We demonstrate that our technique can successfully transfer details among a variety of shapes including furniture and clothing.

@article{Berkiten:2017:LDT,
   author = "Sema Berkiten and Maciej Halber and Justin Solomon and Chongyang Ma and
      Hao Li and Szymon Rusinkiewicz",
   title = "Learning Detail Transfer based on Geometric Features",
   journal = "Computer Graphics Forum (Proc. Eurographics)",
   year = "2017",
   month = apr,
   volume = "36",
   number = "2"
}