People touch their face 23 times an hour, they cross their arms and legs, put
their hands on their hips, etc. While many images of people contain some form
of self-contact, current 3D human pose and shape (HPS) regression methods
typically fail to estimate this contact. To address this, we develop new
datasets and methods that significantly improve human pose estimation with
self-contact. First, we create a dataset of 3D Contact Poses (3DCP) containing
SMPL-X bodies fit to 3D scans as well as poses from AMASS, which we refine to
ensure good contact. Second, we leverage this to create the Mimic-The-Pose
(MTP) dataset of images, collected via Amazon Mechanical Turk, containing
people mimicking the 3DCP poses with selfcontact. Third, we develop a novel HPS
optimization method, SMPLify-XMC, that includes contact constraints and uses
the known 3DCP body pose during fitting to create near ground-truth poses for
MTP images. Fourth, for more image variety, we label a dataset of in-the-wild
images with Discrete Self-Contact (DSC) information and use another new
optimization method, SMPLify-DC, that exploits discrete contacts during pose
optimization. Finally, we use our datasets during SPIN training to learn a new
3D human pose regressor, called TUCH (Towards Understanding Contact in Humans).
We show that the new self-contact training data significantly improves 3D human
pose estimates on withheld test data and existing datasets like 3DPW. Not only
does our method improve results for self-contact poses, but it also improves
accuracy for non-contact poses. The code and data are available for research
purposes at this https URL

本研究通过建立 3D 接触姿势数据集、利用该数据集创建出 Mimic-The-Pose (MTP) 图像数据集多样性，开发了新的优化算法 SMPLify-XMC 和 SMPLify-DC，形成了全新的 3D 人体姿态估计模型 TUCH，并证明该模型显著提高了 3D 人体姿势估计的精确度。