  Despite the recent progress, 3D multi-person pose estimation from monocular
videos is still challenging due to the commonly encountered problem of missing
information caused by occlusion, partially out-of-frame target persons, and
inaccurate person detection. To tackle this problem, we propose a novel
framework integrating graph convolutional networks (GCNs) and temporal
convolutional networks (TCNs) to robustly estimate camera-centric multi-person
3D poses that do not require camera parameters. In particular, we introduce a
human-joint GCN, which, unlike the existing GCN, is based on a directed graph
that employs the 2D pose estimator's confidence scores to improve the pose
estimation results. We also introduce a human-bone GCN, which models the bone
connections and provides more information beyond human joints. The two GCNs
work together to estimate the spatial frame-wise 3D poses and can make use of
both visible joint and bone information in the target frame to estimate the
occluded or missing human-part information. To further refine the 3D pose
estimation, we use our temporal convolutional networks (TCNs) to enforce the
temporal and human-dynamics constraints. We use a joint-TCN to estimate
person-centric 3D poses across frames, and propose a velocity-TCN to estimate
the speed of 3D joints to ensure the consistency of the 3D pose estimation in
consecutive frames. Finally, to estimate the 3D human poses for multiple
persons, we propose a root-TCN that estimates camera-centric 3D poses without
requiring camera parameters. Quantitative and qualitative evaluations
demonstrate the effectiveness of the proposed method.
