  In monocular video 3D multi-person pose estimation, inter-person occlusion
and close interactions can cause human detection to be erroneous and
human-joints grouping to be unreliable. Existing top-down methods rely on human
detection and thus suffer from these problems. Existing bottom-up methods do
not use human detection, but they process all persons at once at the same
scale, causing them to be sensitive to multiple-persons scale variations. To
address these challenges, we propose the integration of top-down and bottom-up
approaches to exploit their strengths. Our top-down network estimates human
joints from all persons instead of one in an image patch, making it robust to
possible erroneous bounding boxes. Our bottom-up network incorporates
human-detection based normalized heatmaps, allowing the network to be more
robust in handling scale variations. Finally, the estimated 3D poses from the
top-down and bottom-up networks are fed into our integration network for final
3D poses. Besides the integration of top-down and bottom-up networks, unlike
existing pose discriminators that are designed solely for single person, and
consequently cannot assess natural inter-person interactions, we propose a
two-person pose discriminator that enforces natural two-person interactions.
Lastly, we also apply a semi-supervised method to overcome the 3D ground-truth
data scarcity. Our quantitative and qualitative evaluations show the
effectiveness of our method compared to the state-of-the-art baselines.
