Learning a human-perceived softness measure of virtual 3D objects

Abstract

We introduce the problem of computing a human-perceived softness measure for virtual 3D objects. As the virtual objects do not exist in the real world, we do not directly consider their physical properties but instead compute the human-perceived softness of the geometric shapes. We collect crowdsourced data where humans rank their perception of the softness of vertex pairs on virtual 3D models. We then compute shape descriptors and use a learning-to-rank approach to learn a softness measure mapping any vertex to a softness value. Finally, we demonstrate our framework with a variety of 3D shapes.