Introducing HOT3D: An Egocentric Dataset for 3D Hand and Object Tracking
This dataset addresses a bottleneck for researchers in computer vision and AR/VR by providing a large-scale, multi-modal resource for 3D hand and object interaction, though it is incremental as it builds on existing data collection efforts.
The authors tackled the problem of limited data for egocentric hand and object tracking in 3D by introducing HOT3D, a publicly available dataset with over 833 minutes of multi-view images, 19 subjects, 33 objects, and comprehensive ground truth annotations, aiming to accelerate research in this area.
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/.