July 2019
Volume 19, Issue 8
Open Access
OSA Fall Vision Meeting Abstract  |   July 2019
Visual Features of Non-Rigid Objects
Author Affiliations
  • Vivian C. Paulun
    Department of Psychology, University of Giessen
  • Filipp Schmidt
    Department of Psychology, University of Giessen
  • Roland W. Fleming
    Department of Psychology, University of Giessen
Journal of Vision July 2019, Vol.19, 91. doi:https://doi.org/10.1167/19.8.91
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      Vivian C. Paulun, Filipp Schmidt, Roland W. Fleming; Visual Features of Non-Rigid Objects. Journal of Vision 2019;19(8):91. https://doi.org/10.1167/19.8.91.

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      © ARVO (1962-2015); The Authors (2016-present)

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Non-rigid objects deform and move in response to external forces. To estimate an object’s softness or elasticity, the visual system has to rapidly disentangle multiple causal contributions. For example, an object deforms strongly either because it is very soft or because the applied force is very large. To investigate how the brain solves this, we simulated and rendered 20 short animations of rigid objects interacting with a non-rigid target. We varied the external force as well as the target’s softness and elasticity and had 15 observers rate the two internal properties. Despite large stimulus variations across external variations, responses were broadly in accordance with the simulated internal properties. However, objects that deformed permanently (i.e. not elastic) were rated as softer. We characterized the visual features of the objects by measuring the deformation, wobbliness, external motion and movement duration of the underlying 3D-meshes. A linear combination of these four features predicts softness perception very well. Next, we simulated over 200.000 animations, massively increasing the variations of internal and external factors. Ten observers rated the softness and elasticity of a small subset of animations. We measured the four features in all 200.000 simulations and fitted a linear regression in order to learn the mappings between visual features and physical material properties. Although the weighted combination of features predicts the physical properties only moderately, the same weights (i.e. without fitting to the perceptual data) predict perceived material properties strikingly well and can account for the perceptual influence of elasticity on softness.


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