Learning Detail Transfer based on Geometric Features

Sema Berkiten, Maciej Halber, Justin Solomon, Chongyang Ma, Hao Li, Szymon Rusinkiewicz

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)361-373
Number of pages13
JournalComputer Graphics Forum
Volume36
Issue number2
DOIs
StatePublished - May 2017

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design

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