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For machines to interact with the physical world, they must understand thephysical properties of objects and materials they encounter. We use fabrics asan example of a deformable material with a rich set of mechanical properties. Athin flexible fabric, when draped, tends to look different from a heavy stifffabric. It also feels different when touched. Using a collection of 118 fabricsample, we captured color and depth images of draped fabrics along with tactiledata from a high resolution touch sensor. We then sought to associate theinformation from vision and touch by jointly training CNNs across the threemodalities. Through the CNN, each input, regardless of the modality, generatesan embedding vector that records the fabric's physical property. By comparingthe embeddings, our system is able to look at a fabric image and predict how itwill feel, and vice versa. We also show that a system jointly trained on visionand touch data can outperform a similar system trained only on visual data whentested purely with visual inputs.

Connecting Look and Feel: Associating the visual and tactile properties of physical materials