Abstract

A first-person video can generate powerful physical sensations of action in an observer. In this paper, we focus on a problem of Force from Motion—decoding the sensation of 1) passive forces such as the gravity, 2) the physical scale ofthe motion (speed) and space, and 3) active forces exerted by the observer such as pedaling a bike or banking on a ski turn. The sensation of gravity can be observed in a natural image. We learn this image cue for predicting a gravity direction in a 2D image and integrate the prediction across images to estimate the 3D gravity direction using structure from motion. The sense of physical scale is revealed to uswhen the body is in a dynamically balanced state. We com-pute the unknown physical scale of 3D reconstructed camera motion by leveraging the torque equilibrium at a banked turn that relates the centripetal force, gravity, and the body leaning angle. The active force and torque governs 3D egomotion through the physics of rigid body dynamics. Using an inverse dynamics optimization, we directly minimize 2D reprojection error (in video) with respect to 3D world structure, active forces, and additional passive forces such as air drag and friction force. We use structure from motion with the physical scale and gravity direction as an initialization of our bundle adjustment for force estimation. Our method shows quantitatively equivalent reconstruction comparing to IMU measurements in terms of gravity and scale recovery and outperforms method based on 2D optical flow for an active action recognition task. We apply our method to first person videos of mountain biking, urban bike racing, skiing, speedflying with parachute, and wingsuit flying where inertial measurements are not accessible.