HO-Cap: A Capture System and Dataset for 3D Reconstruction and Pose Tracking of Hand-Object Interaction
CoRR(2024)
摘要
We introduce a data capture system and a new dataset named HO-Cap that can be
used to study 3D reconstruction and pose tracking of hands and objects in
videos. The capture system uses multiple RGB-D cameras and a HoloLens headset
for data collection, avoiding the use of expensive 3D scanners or mocap
systems. We propose a semi-automatic method to obtain annotations of shape and
pose of hands and objects in the collected videos, which significantly reduces
the required annotation time compared to manual labeling. With this system, we
captured a video dataset of humans using objects to perform different tasks, as
well as simple pick-and-place and handover of an object from one hand to the
other, which can be used as human demonstrations for embodied AI and robot
manipulation research. Our data capture setup and annotation framework can be
used by the community to reconstruct 3D shapes of objects and human hands and
track their poses in videos.
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