FAME: 3D Shape Generation via Functionality-Aware Model Evolution

Yanran Guan, Han Liu, Kun Liu, Kangxue Yin, Ruizhen Hu, Oliver vanKaick, Yan Zhang, Ersin Yumer, Nathan Carr, Radomir Mech, Richard Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

We introduce a modeling tool which can evolve a set of 3D objects in a functionality-aware manner. Our goal is for the evolution to generate large and diverse sets of plausible 3D objects for data augmentation, constrained modeling, as well as open-ended exploration to possibly inspire new designs. Starting with an initial population of 3D objects belonging to one or more functional categories, we evolve the shapes through part re-combination to produce generations of hybrids or crossbreeds between parents from the heterogeneous shape collection. Evolutionary selection of offsprings is guided both by a functional plausibility score derived from functionality analysis of shapes in the initial population and user preference, as in a design gallery. Since cross-category hybridization may result in offsprings not belonging to any of the known functional categories, we develop a means for functionality partial matching to evaluate functional plausibility on partial shapes. We show a variety of plausible hybrid shapes generated by our functionality-aware model evolution, which can complement existing datasets as training data and boost the performance of contemporary data-driven segmentation schemes, especially in challenging cases.
Original languageEnglish (US)
Pages (from-to)1-1
Number of pages1
JournalIEEE Transactions on Visualization and Computer Graphics
DOIs
StatePublished - Oct 12 2020

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