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Computational models reveal that intuitive physics underlies visual processing of soft objects.

Wenyan Bi1, Aalap D Shah2, Kimberly W Wong2

  • 1Department of Psychology, Yale University, New Haven, CT, USA. wenyan.bi@yale.edu.

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New computational models can now perceive soft objects like cloth, mimicking human visual perception. The Woven model uses physics simulations to understand cloth properties, outperforming existing methods.

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Area of Science:

  • Cognitive Science
  • Computer Vision
  • Physics Simulation

Background:

  • Computational models excel at rigid object perception, focusing on invariance.
  • Soft objects like cloth present unique challenges due to their dynamic, high-dimensional nature.
  • Perceiving soft objects involves understanding properties like stiffness and deformation.

Purpose of the Study:

  • To evaluate computational models' ability to perceive physical properties of soft objects (cloth).
  • To compare model performance against human visual perception of cloth transformations.
  • To introduce a novel model, Woven, for enhanced soft object perception.

Main Methods:

  • Developed the Woven model incorporating physics-based simulations.
  • Used probabilistic representations to infer cloth properties.
  • Tested models on visual matching tasks involving naturalistic cloth transformations.

Main Results:

  • The Woven model successfully explained human performance in visual matching tasks involving cloth.
  • Woven outperformed alternative models, including deep neural networks.
  • Model failures also mirrored human perceptual limitations.

Conclusions:

  • Humanlike machine vision may require intuitive physics representations beyond image statistics.
  • Physics-based simulations are crucial for accurate soft object perception.
  • The Woven model offers a promising approach to understanding complex material perception.