Learning a Human-Perceived Softness Measure of Virtual Objects

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-torank approach to learn a softness measure mapping any vertex to
a softness value. Finally, we demonstrate the accuracy and robustness of our framework with a user study and a variety of 3D shapes,
and show an application of fabricating virtual 3D objects.