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.

本文提出了一种自接触预测模型（SCP），并采用特定的 3D 损失来显式地强制进行自接触，以解决现有 3D 重建方法对于身体自接触区域的忽略问题，并在自接触特征限制下研究更具表现力的 3D 重建，最终在多视图图像上提出一种面部接触的单目检测方法。

学习复杂三维人体自我接触

Learning Complex 3D Human Self-Contact

Monocular estimation of 3d human pose has attracted increased attention with
the availability of large ground-truth motion capture datasets. However, the
diversity of training data available is limited and it is not clear to what
extent methods generalize outside the specific datasets they are trained on. In
this work we carry out a systematic study of the diversity and biases present
in specific datasets and its effect on cross-dataset generalization across a
compendium of 5 pose datasets. We specifically focus on systematic differences
in the distribution of camera viewpoints relative to a body-centered coordinate
frame. Based on this observation, we propose an auxiliary task of predicting
the camera viewpoint in addition to pose. We find that models trained to
jointly predict viewpoint and pose systematically show significantly improved
cross-dataset generalization.

本研究采用系统性研究来分析不同数据集之间多样性和偏差对模型泛化能力的影响，提出了一种同时预测相机视角和人体姿态的模型，取得了显著的跨数据集泛化性能提升。