  We present an approach for 3D global human mesh recovery from monocular
videos recorded with dynamic cameras. Our approach is robust to severe and
long-term occlusions and tracks human bodies even when they go outside the
camera's field of view. To achieve this, we first propose a deep generative
motion infiller, which autoregressively infills the body motions of occluded
humans based on visible motions. Additionally, in contrast to prior work, our
approach reconstructs human meshes in consistent global coordinates even with
dynamic cameras. Since the joint reconstruction of human motions and camera
poses is underconstrained, we propose a global trajectory predictor that
generates global human trajectories based on local body movements. Using the
predicted trajectories as anchors, we present a global optimization framework
that refines the predicted trajectories and optimizes the camera poses to match
the video evidence such as 2D keypoints. Experiments on challenging indoor and
in-the-wild datasets with dynamic cameras demonstrate that the proposed
approach outperforms prior methods significantly in terms of motion infilling
and global mesh recovery.
