Pose-Inspired Shape Synthesis and Functional Hybrid

We introduce a shape synthesis approach especially for functional hybrid creation that can be potentially used by a human operator under a certain pose. Shape synthesis by reusing parts in existing models has been an active research topic in recent years. However, how to combine models across different categories to design multi-function objects remains challenging, since there is no natural correspondence between models across different categories. We tackle this problem by introducing a human pose to describe object affordance which establishes a bridge between cross-class objects for composite design. Specifically, our approach first identifies groups of candidate shapes which provide affordances desired by an input human pose, and then recombines them as well-connected composite models. Users may control the design process by manipulating the input pose, or optionally specifying one or more desired categories. We also extend our approach to be used by a single operator with multiple poses or by multiple human operators. We show that our approach enables easy creation of nontrivial, interesting synthesized models.