  Understanding 3d human interactions is fundamental for fine-grained scene
analysis and behavioural modeling. However, most of the existing models predict
incorrect, lifeless 3d estimates, that miss the subtle human contact
aspects--the essence of the event--and are of little use for detailed
behavioral understanding. This paper addresses such issues with several
contributions: (1) we introduce models for interaction signature estimation
(ISP) encompassing contact detection, segmentation, and 3d contact signature
prediction; (2) we show how such components can be leveraged to ensure contact
consistency during 3d reconstruction; (3) we construct several large datasets
for learning and evaluating 3d contact prediction and reconstruction methods;
specifically, we introduce CHI3D, a lab-based accurate 3d motion capture
dataset with 631 sequences containing $2,525$ contact events, $728,664$ ground
truth 3d poses, as well as FlickrCI3D, a dataset of $11,216$ images, with
$14,081$ processed pairs of people, and $81,233$ facet-level surface
correspondences. Finally, (4) we propose methodology for recovering the
ground-truth pose and shape of interacting people in a controlled setup and (5)
annotate all 3d interaction motions in CHI3D with textual descriptions. Motion
data in multiple formats (GHUM and SMPLX parameters, Human3.6m 3d joints) is
made available for research purposes at \url{https://ci3d.imar.ro}, together
with an evaluation server and a public benchmark.
