  Grasping is natural for humans. However, it involves complex hand
configurations and soft tissue deformation that can result in complicated
regions of contact between the hand and the object. Understanding and modeling
this contact can potentially improve hand models, AR/VR experiences, and
robotic grasping. Yet, we currently lack datasets of hand-object contact paired
with other data modalities, which is crucial for developing and evaluating
contact modeling techniques. We introduce ContactPose, the first dataset of
hand-object contact paired with hand pose, object pose, and RGB-D images.
ContactPose has 2306 unique grasps of 25 household objects grasped with 2
functional intents by 50 participants, and more than 2.9 M RGB-D grasp images.
Analysis of ContactPose data reveals interesting relationships between hand
pose and contact. We use this data to rigorously evaluate various data
representations, heuristics from the literature, and learning methods for
contact modeling. Data, code, and trained models are available at
https://contactpose.cc.gatech.edu.
