  We introduce HOT3D, a publicly available dataset for egocentric hand and
object tracking in 3D. The dataset offers over 833 minutes (more than 3.7M
images) of multi-view RGB/monochrome image streams showing 19 subjects
interacting with 33 diverse rigid objects, multi-modal signals such as eye gaze
or scene point clouds, as well as comprehensive ground truth annotations
including 3D poses of objects, hands, and cameras, and 3D models of hands and
objects. In addition to simple pick-up/observe/put-down actions, HOT3D contains
scenarios resembling typical actions in a kitchen, office, and living room
environment. The dataset is recorded by two head-mounted devices from Meta:
Project Aria, a research prototype of light-weight AR/AI glasses, and Quest 3,
a production VR headset sold in millions of units. Ground-truth poses were
obtained by a professional motion-capture system using small optical markers
attached to hands and objects. Hand annotations are provided in the UmeTrack
and MANO formats and objects are represented by 3D meshes with PBR materials
obtained by an in-house scanner. We aim to accelerate research on egocentric
hand-object interaction by making the HOT3D dataset publicly available and by
co-organizing public challenges on the dataset at ECCV 2024. The dataset can be
downloaded from the project website: https://facebookresearch.github.io/hot3d/.
