  Touch plays a fundamental role in manipulation for humans; however, machine
perception of contact and pressure typically requires invasive sensors. Recent
research has shown that deep models can estimate hand pressure based on a
single RGB image. However, evaluations have been limited to controlled settings
since collecting diverse data with ground-truth pressure measurements is
difficult. We present a novel approach that enables diverse data to be captured
with only an RGB camera and a cooperative participant. Our key insight is that
people can be prompted to apply pressure in a certain way, and this prompt can
serve as a weak label to supervise models to perform well under varied
conditions. We collect a novel dataset with 51 participants making fingertip
contact with diverse objects. Our network, PressureVision++, outperforms human
annotators and prior work. We also demonstrate an application of
PressureVision++ to mixed reality where pressure estimation allows everyday
surfaces to be used as arbitrary touch-sensitive interfaces. Code, data, and
models are available online.
