Perceptual constancy of mechanical properties of cloth under variation of external forces

Abstract

Deformable objects such as cloth exhibit their mechanical properties (e.g. stiffness) through shape deformation over time under external forces. Mechanical properties are important because they tell us the affordance of the object and helps us predict what type of action can be done upon it. Previous research shows that motion statistics can be used to develop computer vision algorithms to estimate mechanical properties of cloth under an unknown wind force. It is unclear what motion cues human use to estimate mechanical properties. Estimating mechanical properties is difficult because both the intrinsic properties of the fabric and the external force contribute to the apparent motion of the fabric. However, in order to achieve invariant material perception, the visual system needs to discount the effects of external force. In this paper, we investigate whether humans have an invariant representation of mechanical properties of fabrics under varying external forces in dynamic scenes. Then we study what visual cues allow humans to achieve this perceptual constancy. The stimuli are animated videos containing a hanging fabric moving under oscillating wind. We vary both intrinsic mechanical properties such as mass and stiffness of the cloth as well as the strength of the wind force. We discuss our results in the context of optical flow statistics. This advances the current understanding of the role of motion in perception of material properties in dynamic scenes.