  Monocular estimation of three dimensional human self-contact is fundamental
for detailed scene analysis including body language understanding and behaviour
modeling. Existing 3d reconstruction methods do not focus on body regions in
self-contact and consequently recover configurations that are either far from
each other or self-intersecting, when they should just touch. This leads to
perceptually incorrect estimates and limits impact in those very fine-grained
analysis domains where detailed 3d models are expected to play an important
role. To address such challenges we detect self-contact and design 3d losses to
explicitly enforce it. Specifically, we develop a model for Self-Contact
Prediction (SCP), that estimates the body surface signature of self-contact,
leveraging the localization of self-contact in the image, during both training
and inference. We collect two large datasets to support learning and
evaluation: (1) HumanSC3D, an accurate 3d motion capture repository containing
$1,032$ sequences with $5,058$ contact events and $1,246,487$ ground truth 3d
poses synchronized with images collected from multiple views, and (2)
FlickrSC3D, a repository of $3,969$ images, containing $25,297$
surface-to-surface correspondences with annotated image spatial support. We
also illustrate how more expressive 3d reconstructions can be recovered under
self-contact signature constraints and present monocular detection of
face-touch as one of the multiple applications made possible by more accurate
self-contact models.
