A Human-Perceived Softness Measure of Virtual 3D Objects

Lau, Manfred Chung Man and Dev, Kapil and Dorsey, Julie and Rushmeier, Holly (2018) A Human-Perceived Softness Measure of Virtual 3D Objects. ACM Transactions on Applied Perception, 15 (3). ISSN 1544-3558

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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. In an initial experiment, we find that humans are highly consistent in their responses when given a pair of vertices on a 3D model and asked to select the vertex that they perceive to be more soft. This motivates us to take a crowdsourcing and machine learning framework. We collect crowdsourced data for such pairs of vertices. We then combine a learning-to-rank approach and a multi-layer neural network to learn a non-linear softness measure mapping any vertex to a softness value. For a new 3D shape, we can use the learned measure to compute the relative softness of every vertex on its surface. We demonstrate the robustness of our framework with a variety of 3D shapes and compare our non-linear learning approach with a linear method from previous work. Finally, we demonstrate the accuracy of our learned measure with user studies comparing our measure with the human-perceived softness of both virtual and real objects, and we show the usefulness of our measure with some applications.

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Journal Article
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ACM Transactions on Applied Perception
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© ACM, 2018. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Applied Perception (TAP) http://dx.doi.org/10.1145/3193107
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16 May 2018 14:30
Last Modified:
20 Sep 2023 01:09