  Current unsupervised 2D-3D human pose estimation (HPE) methods do not work in
multi-person scenarios due to perspective ambiguity in monocular images.
Therefore, we present one of the first studies investigating the feasibility of
unsupervised multi-person 2D-3D HPE from just 2D poses alone, focusing on
reconstructing human interactions. To address the issue of perspective
ambiguity, we expand upon prior work by predicting the cameras' elevation angle
relative to the subjects' pelvis. This allows us to rotate the predicted poses
to be level with the ground plane, while obtaining an estimate for the vertical
offset in 3D between individuals. Our method involves independently lifting
each subject's 2D pose to 3D, before combining them in a shared 3D coordinate
system. The poses are then rotated and offset by the predicted elevation angle
before being scaled. This by itself enables us to retrieve an accurate 3D
reconstruction of their poses. We present our results on the CHI3D dataset,
introducing its use for unsupervised 2D-3D pose estimation with three new
quantitative metrics, and establishing a benchmark for future research.
