  We propose TRAM, a two-stage method to reconstruct a human's global
trajectory and motion from in-the-wild videos. TRAM robustifies SLAM to recover
the camera motion in the presence of dynamic humans and uses the scene
background to derive the motion scale. Using the recovered camera as a
metric-scale reference frame, we introduce a video transformer model (VIMO) to
regress the kinematic body motion of a human. By composing the two motions, we
achieve accurate recovery of 3D humans in the world space, reducing global
motion errors by 60% from prior work. https://yufu-wang.github.io/tram4d/
