PressureVision: Estimating Hand Pressure from a Single RGB Image
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People often interact with their surroundings by applying pressure with their hands. Machine perception of hand pressure has been limited by the challenges of placing sensors between the hand and the contact surface. We explore the possibility of using a conventional RGB camera to infer hand pressure. The central insight is that the application of pressure by a hand results in informative appearance changes. Hands share biomechanical properties that result in similar observable phenomena, such as soft-tissue deformation, blood distribution, hand pose, and cast shadows. We collected videos of 36 participants with diverse skin tone applying pressure to an instrumented planar surface. We then trained a deep model (PressureVisionNet) to infer a pressure image from a single RGB image. Our model infers pressure for participants outside of the training data and outperforms baselines. We also show that the output of our model depends on the appearance of the hand and cast shadows near contact regions. Overall, our results suggest the appearance of a previously unobserved human hand can be used to accurately infer applied pressure.
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