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