  To gain an understanding of the relation between a given human pose image and
the corresponding physical foot pressure of the human subject, we propose and
validate two end-to-end deep learning architectures, PressNet and
PressNet-Simple, to regress foot pressure heatmaps (dynamics) from 2D human
pose (kinematics) derived from a video frame. A unique video and foot pressure
data set of 813,050 synchronized pairs, composed of 5-minute long choreographed
Taiji movement sequences of 6 subjects, is collected and used for
leaving-one-subject-out cross validation. Our initial experimental results
demonstrate reliable and repeatable foot pressure prediction from a single
image, setting the first baseline for such a complex cross modality mapping
problem in computer vision. Furthermore, we compute and quantitatively validate
the Center of Pressure (CoP) and Base of Support (BoS) from predicted foot
pressure distribution, obtaining key components in pose stability analysis from
images with potential applications in kinesiology, medicine, sports and
robotics.
